Dissertations / Theses on the topic 'Signal estimation'
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Patriksson, Alfred. "Radio signal DOA estimation : Implementing radar signal direction estimation on an FPGA." Thesis, Linköpings universitet, Datorteknik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-157144.
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This master’s thesis covers the design and implementation of a monopulse directionof arrival (DOA) estimation algorithm on an FPGA. The goal is to implement a complete system that is capable of estimating the bearing of an incident signal. In order to determine the estimate quality both a theoretical and practical noise analysis of the signal chain is performed. Special focus is placed on the statistical properties of the transformation from I/Q-demodulated signals with correlated noise to a polar representation. The pros and cons for three different methods of calculating received signal phasors are also covered.The system is limited to two receiving channels which constrains this report to a 2D analysis. In addition the used hardware is limited to C-band signals. We show that an FPGA implementation of monopulse techniques is definitely viable and that an SNR higher than ten dB allows for a gaussian approximation of the polar representationof an I/Q signal.
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Mabrouk, Mohamed Hussein Emam Mabrouk. "Signal Processing of UWB Radar Signals for Human Detection Behind Walls." Thesis, Université d'Ottawa / University of Ottawa, 2015. http://hdl.handle.net/10393/31945.
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Non-contact life detection is a significant component of both civilian and military rescue applications. As a consequence, this interest has resulted in a very active area of research. The primary goal of this research is reliable detection of a human breathing signal. Additional goals of this research are to carry out detection under realistic conditions, to distinguish between two targets, to determine human breathing rate and estimate the posture. Range gating and Singular Value Decomposition (SVD) have been used to remove clutter in order to detect human breathing under realistic conditions. However, the information of the target range or what principal component contains target information may be unknown. DFT and Short Time Fourier Transform (STFT) algorithms have been used to detect the human breathing and discriminate between two targets. However, the algorithms result in many false alarms because they detect breathing when no target exists. The unsatisfactory performance of the DFT-based estimators in human breathing rate estimation is due to the fact that the second harmonic of the breathing signal has higher magnitude than the first harmonic. Human posture estimation has been performed by measuring the distance of the chest displacements from the ground. This requires multiple UWB receivers and a more complex system. In this thesis, monostatic UWB radar is used. Initially, the SVD method was combined with the skewness test to detect targets, discriminate between two targets, and reduce false alarms. Then, a novel human breathing rate estimation algorithm was proposed using zero-crossing method. Subsequently, a novel method was proposed to distinguish between human postures based on the ratios between different human breathing frequency harmonics magnitudes. It was noted that the ratios depend on the abdomen displacements and higher harmonic ratios were observed when the human target was sitting or standing. The theoretical analysis shows that the distribution of the skewness values of the correlator output of the target and the clutter signals in a single range-bin do not overlap. The experimental results on human breathing detection, breathing rate, and human posture estimation show that the proposed methods improve performance in human breathing detection and rate estimation.
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Haghighi-Mood, Ali. "Analysis of phonocardiographic signals using advanced signal processing techniques." Thesis, University of Sussex, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.321465.
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Mahata, Kaushik. "Estimation Using Low Rank Signal Models." Doctoral thesis, Uppsala University, Department of Information Technology, 2003. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-3844.
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Designing estimators based on low rank signal models is a common practice in signal processing. Some of these estimators are designed to use a single low rank snapshot vector, while others employ multiple snapshots. This dissertation deals with both these cases in different contexts.
Separable nonlinear least squares is a popular tool to extract parameter estimates from a single snapshot vector. Asymptotic statistical properties of the separable non-linear least squares estimates are explored in the first part of the thesis. The assumptions imposed on the noise process and the data model are general. Therefore, the results are useful in a wide range of applications. Sufficient conditions are established for consistency, asymptotic normality and statistical efficiency of the estimates. An expression for the asymptotic covariance matrix is derived and it is shown that the estimates are circular. The analysis is extended also to the constrained separable nonlinear least squares problems.
Nonparametric estimation of the material functions from wave propagation experiments is the topic of the second part. This is a typical application where a single snapshot vector is employed. Numerical and statistical properties of the least squares algorithm are explored in this context. Boundary conditions in the experiments are used to achieve superior estimation performance. Subsequently, a subspace based estimation algorithm is proposed. The subspace algorithm is not only computationally efficient, but is also equivalent to the least squares method in accuracy.
Estimation of the frequencies of multiple real valued sine waves is the topic in the third part, where multiple snapshots are employed. A new low rank signal model is introduced. Subsequently, an ESPRIT like method named R-Esprit and a weighted subspace fitting approach are developed based on the proposed model. When compared to ESPRIT, R-Esprit is not only computationally more economical but is also equivalent in performance. The weighted subspace fitting approach shows significant improvement in the resolution threshold. It is also robust to additive noise.
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Chen, Hao. "Noise enhanced signal detection and estimation." Related electronic resource:, 2007. http://proquest.umi.com/pqdweb?did=1342743841&sid=2&Fmt=2&clientId=3739&RQT=309&VName=PQD.
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Warner, Carl Michael 1952. "ESTIMATION OF NONSTATIONARY SIGNALS IN NOISE (PROCESSING, ADAPTIVE, WIENER FILTERS, ESTIMATION, DIGITAL)." Thesis, The University of Arizona, 1986. http://hdl.handle.net/10150/291297.
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常春起 and Chunqi Chang. "Blind signal estimation using second order statistics." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2000. http://hub.hku.hk/bib/B31241487.
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Chang, Chunqi. "Blind signal estimation using second order statistics /." Hong Kong : University of Hong Kong, 2000. http://sunzi.lib.hku.hk/hkuto/record.jsp?B23272806.
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Lee, Joonsung. "Acoustic signal estimation using multiple blind observations." Thesis, Massachusetts Institute of Technology, 2006. http://hdl.handle.net/1721.1/35603.
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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2006.Includes bibliographical references (p. 109-111).
This thesis proposes two algorithms for recovering an acoustic signal from multiple blind measurements made by sensors (microphones) over an acoustic channel. Unlike other algorithms that use a posteriori probabilistic models to fuse the data in this problem, the proposed algorithms use results obtained in the context of data communication theory. This constitutes a new approach to this sensor fusion problem. The proposed algorithms determine inverse channel filters with a predestined support (number of taps). The Coordinated Recovery of Signals From Sensors (CROSS) algorithm is an indirect method, which uses an estimate of the acoustic channel. Using the estimated channel coefficients from a Least-Squares (LS) channel estimation method, we propose an initialization process (zero-forcing estimate) and an iteration process (MMSE estimate) to produce optimal inverse filters accounting for the room characteristics, additive noise and errors in the estimation of the parameters of the room characteristics.
(cont.) Using a measured room channel, we analyze the performance of the algorithm through simulations and compare its performance with the theoretical performance. Also, in this thesis, the notion of channel diversity is generalized and the Averaging Row Space Intersection (ARSI) algorithm is proposed. The ARSI algorithm is a direct method, which does not use the channel estimate.
by Joonsung Lee.
S.M.
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Kanagasabapathy, Shri. "Distributed adaptive signal processing for frequency estimation." Thesis, Imperial College London, 2016. http://hdl.handle.net/10044/1/49783.
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It is widely recognised that future smart grids will heavily rely upon intelligent communication and signal processing as enabling technologies for their operation. Traditional tools for power system analysis, which have been built from a circuit theory perspective, are a good match for balanced system conditions. However, the unprecedented changes that are imposed by smart grid requirements, are pushing the limits of these old paradigms. To this end, we provide new signal processing perspectives to address some fundamental operations in power systems such as frequency estimation, regulation and fault detection. Firstly, motivated by our finding that any excursion from nominal power system conditions results in a degree of non-circularity in the measured variables, we cast the frequency estimation problem into a distributed estimation framework for noncircular complex random variables. Next, we derive the required next generation widely linear, frequency estimators which incorporate the so-called augmented data statistics and cater for the noncircularity and a widely linear nature of system functions. Uniquely, we also show that by virtue of augmented complex statistics, it is possible to treat frequency tracking and fault detection in a unified way. To address the ever shortening time-scales in future frequency regulation tasks, the developed distributed widely linear frequency estimators are equipped with the ability to compensate for the fewer available temporal voltage data by exploiting spatial diversity in wide area measurements. This contribution is further supported by new physically meaningful theoretical results on the statistical behavior of distributed adaptive filters. Our approach avoids the current restrictive assumptions routinely employed to simplify the analysis by making use of the collaborative learning strategies of distributed agents. The efficacy of the proposed distributed frequency estimators over standard strictly linear and stand-alone algorithms is illustrated in case studies over synthetic and real-world three-phase measurements. An overarching theme in this thesis is the elucidation of underlying commonalities between different methodologies employed in classical power engineering and signal processing. By revisiting fundamental power system ideas within the framework of augmented complex statistics, we provide a physically meaningful signal processing perspective of three-phase transforms and reveal their intimate connections with spatial discrete Fourier transform (DFT), optimal dimensionality reduction and frequency demodulation techniques. Moreover, under the widely linear framework, we also show that the two most widely used frequency estimators in the power grid are in fact special cases of frequency demodulation techniques. Finally, revisiting classic estimation problems in power engineering through the lens of non-circular complex estimation has made it possible to develop a new self-stabilising adaptive three-phase transformation which enables algorithms designed for balanced operating conditions to be straightforwardly implemented in a variety of real-world unbalanced operating conditions. This thesis therefore aims to help bridge the gap between signal processing and power communities by providing power system designers with advanced estimation algorithms and modern physically meaningful interpretations of key power engineering paradigms in order to match the dynamic and decentralised nature of the smart grid.
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Qu, Yang. "Mixed Signal Detection, Estimation, and Modulation Classification." Wright State University / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=wright1576615989584971.
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Hwang, Suk-seung, John J. Shynk, and Hua Lee. "Efficient AOA Estimation Techniques for GPS Signal." International Foundation for Telemetering, 2015. http://hdl.handle.net/10150/596458.
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ITC/USA 2015 Conference Proceedings / The Fifty-First Annual International Telemetering Conference and Technical Exhibition / October 26-29, 2015 / Bally's Hotel & Convention Center, Las Vegas, NVGlobal Positioning System (GPS) interference signals are suppressed using angle-of-arrival (AOA) techniques, while at the same time the power of the GPS signal is enhanced. After estimating all AOAs from the received signal, we must determine which AOA corresponds to the GPS signal of interest, and in the presence of high-power interference signals. In this paper, we describe an algorithm for selecting the GPS AOA by first comparing all AOAs derived from the received signals before despreading. Although this approach has excellent performance, it has a high computational complexity. In order to overcome this drawback, we introduce a modification that yields an efficient GPS AOA estimation algorithm, which is based on a modified despreader and the constant modulus (CM) array cost function. The CM array is capable of selecting signals that have a constant modulus while rejecting non-CM interference signals. The modified despreader is the mechanism that allows this to be achieved, where unlike the interference signals, the GPS signal of interest maintains a constant modulus.
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Hassana, Ramesh Rakesh Kashyap. "Transform Domain Acquisition of Spread Spectrum Signals in a Low Signal to Noise Ratio Environment." Ohio University / OhioLINK, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1289579500.
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Beek, Jaap van de. "Estimation of synchronization parameters." Licentiate thesis, Luleå tekniska universitet, Signaler och system, 1996. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-16971.
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This thesis deals with the estimation of synchronization parameters in {Orthogonal Frequency Division Multiplexing} (OFDM) communication systems and in active ultrasonic measuring systems. Estimation methods for the timing and frequency offset and for the attenuation taps of the frequency selective channel are presented and investigated.In OFDM communication systems the estimation of the timing offset of the transmitted data frame is one important parameter. This offset provides the receiver with a means of synchronizing its sampling clock to that of the transmitter. A second important parameter is the offset in the carrier frequency used by the receiver to demodulate the received signal.For OFDM systems using a cyclic prefix, the joint {Maximum Likelihood} (ML) estimation of the timing and carrier frequency offset is introduced. The redundancy introduced by the prefix is exploited optimally. This novel method is derived for a non-dispersive channel. Its performance, however, is also evaluated for a frequency-selective Rayleigh-fading radio channel. Time dispersion causes an irreducible error floor in this estimator's performance. This error floor is the limiting factor for the applicability of the timing estimator. Depending on the requirements, it may be used in either an acquisition or a tracking mode. For the frequency estimator the error floor is low enough to allow for stable frequency tracking.A low-complex variant of the timing offset estimator is presented allowing a simple implementation. This is the ML estimator, given a 2-bit representation of the received signal as the sufficient statistics. Its performance is evaluated for a frequency-selective Rayleigh-fading radio channel and for a twisted-pair copper channel. Simulations show this estimator to have a similar error floor as the full resolution ML estimator.The problem of estimating the propagation time of a signal is also of interest in active pulse echo systems, such as are used in, {\it e.g.}, radar, medical imaging, and geophysics. The {Minimum Mean Squared Error} (MMSE) estimator of arrival time is derived and investigated for an active airborne ultrasound measurement system. Besides performing better than the conventional {\it Maximum a Posteriori} (MAP) estimator, this method can be used to develop different estimators in situations where the system Signal to Noise Ratio (SNR) is unknown.Coherent multi-amplitude OFDM receivers generally need to compensate for a frequency selective channel in order to detect transmitted data symbols reliably. For this purpose, a channel equalizer needs to be fed estimates of the subchannel attenuations.The linear MMSE estimator of these attenuations is presented. Of all linear estimators, this estimator optimally makes use of the frequency correlation between the subchannel attenuations. Low-complex modified estimators are proposed and investigated. The proposed modifications cause an irreducible error floor for this estimator's performance, but simulations show that for SNR values up to 20~dB, the improvement of a modified estimator compared to the Least Squares (LS) estimator is at least 3~dB.Godkänd; 1996; 20080328 (ysko)
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Cherif, Safa. "Effective signal processing methods for robust respiratory rate estimation from photoplethysmography signal." Thesis, Ecole nationale supérieure Mines-Télécom Atlantique Bretagne Pays de la Loire, 2018. http://www.theses.fr/2018IMTA0094/document.
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Le photopléthysmogramme (PPG) est un signal optique acquis à partir de l’oxymètre de pouls, dont l’usage principal consiste à mesurer la saturation en oxygène. Avec le développement des technologies portables, il est devenu la technique de base pour la surveillance de l’activité cardio-respiratoire des patients et la détection des anomalies. En dépit de sa simplicité d'utilisation, le déploiement de cette technique reste encore limité pour deux principales raisons : 1. L’extrême sensibilité du signal aux distorsions. 2. La non-reproductibilité entre les sujets et pour les mêmes sujets, en raison de l'âge et des conditions de santé. L’objectif de cette thèse est le développement de méthodes robustes et universelles afin d’avoir une estimation précise de la fréquence respiratoire (FR) indépendamment de la variabilité intra et interindividuelle du PPG. Plusieurs contributions originales en traitement statistiques du signal PPG sont proposées. En premier lieu, une méthode adaptative de détection des artefacts basée sur la comparaison de modèle a été développée. Des tests par la technique Random Distortion Testing sont introduits pour détecter les pulses de PPG avec des artefacts. En deuxième lieu, une analyse de plusieurs méthodes spectrales d’estimation de la FR est proposée. Afin de mettre en évidence la robustesse des méthodes proposées face à la variabilité du signal, plusieurs tests ont été effectués sur deux bases de données avec de différentes tranches d'âge et des différents modes respiratoires. En troisième lieu, un indice de qualité respiratoire spectral (SRQI) est conçu dans le but de communiquer au clinicien que les valeurs fiables de la FR avec un certain degré de confianceOne promising area of research in clinical routine involves using photoplethysmography (PPG) for monitoring respiratory activities. PPG is an optical signal acquired from oximeters, whose principal use consists in measuring oxygen saturation. Despite its simplicity of use, the deployment of this technique is still limited because of the signal sensitivity to distortions and the non-reproducibility between subjects, but also for the same subject, due to age and health conditions. The main aim of this work is to develop robust and universal methods for estimating accurate respiratory rate regardless of the intra- and inter-individual variability that affects PPG features. For this purpose, firstly, an adaptive artefact detection method based on template matching and decision by Random Distortion Testing is introduced for detecting PPG pulses with artefacts. Secondly, an analysis of several spectral methods for Respiratory Rate (RR) estimation on two different databases, with different age ranges and different respiratory modes, is proposed. Thirdly, a Spectral Respiratory Quality Index (SRQI) is attributed to respiratory rate estimates, in order that the clinician may select only RR values with a large confidence scale. Promising results are found for two different databases
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Mason, Steven George. "A modification of OPM : a signal-independent methodology for single-trial signal extraction." Thesis, University of British Columbia, 1990. http://hdl.handle.net/2429/30024.
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Initial investigations of the Outlier Processing Method (OPM), first introduced by Birch [1][2][3] in 1988, have demonstrated a promising ability to extract a special class of signals, called highly variable events (HVEs), from coloured noise processes. The term HVE is introduced in this thesis to identify a finite-duration signal whose shape and latency vary dramatically from trial to trial and typically has a very low signal-to-noise ratio (SNR).
This thesis presents a modified version of the original OPM algorithm, which can generate an estimate of the HVE with significantly less estimation noise than the original OPM algorithm. Simulation experiments are used to identify the strengths and limitations of this modified OPM algorithm for linear and stationary processes and to compare the modified algorithm's performance to the performance of the original algorithm and to the performance of a minimum mean-square-error (MMSE) filter. The results of these experiments verify that the modified algorithm can extract an HVE with less estimation noise than the original algorithm. The results also show, that the MMSE filter is unsuitable for extracting HVEs and that its performance is generally inferior to the modified algorithm's performance. The experiments indicate that the modified algorithm can extract HVEs from a linear and stationary process for SNR levels above -2.5dB and can work effectively above -7.5dB for HVEs with certain characteristics.Applied Science, Faculty of
Electrical and Computer Engineering, Department of
Graduate
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Lindfors, Martin. "Frequency Tracking for Speed Estimation." Licentiate thesis, Linköpings universitet, Reglerteknik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-149804.
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Estimating the frequency of a periodic signal, or tracking the time-varying frequency of an almost periodic signal, is an important problem that is well studied in literature. This thesis focuses on two subproblems where contributions can be made to the existing theory: frequency tracking methods and measurements containing outliers. Maximum-likelihood-based frequency estimation methods are studied, focusing on methods which can handle outliers in the measurements. Katkovnik’s frequency estimation method is generalized to real and harmonic signals, and a new method based on expectation-maximization is proposed. The methods are compared in a simulation study in which the measurements contain outliers. The proposed methods are compared with the standard periodogram method. Recursive Bayesian methods for frequency tracking are studied, focusing on the Rao-Blackwellized point mass filter (RBPMF). Two reformulations of the RBPMF aiming to reduce computational costs are proposed. Furthermore, the technique of variational approximate Rao-Blackwellization is proposed, which allows usage of a Student’s t distributed measurement noise model. This enables recursive frequency tracking methods to handle outliers using heavy-tailed noise models in Rao-Blackwellized filters such as the RBPMF. A simulation study illustrates the performance of the methods when outliers occur in the measurement noise. The framework above is applied to and studied in detail in two applications. The first application is on frequency tracking of engine sound. Microphone measurements are used to track the frequency of Doppler-shifted variants of the engine sound of a vehicle moving through an area. These estimates can be used to compute the speed of the vehicle. Periodogram-based methods and the RBPMF are evaluated on simulated and experimental data. The results indicate that the RBPMF has lower rmse than periodogram-based methods when tracking fast changes in the frequency. The second application relates to frequency tracking of wheel vibrations, where a car has been equipped with an accelerometer. The accelerometer measurements are used to track the frequency of the wheel axle vibrations, which relates to the wheel rotational speed. The velocity of the vehicle can then be estimated without any other sensors and without requiring integration of the accelerometer measurements. In situations with high signal-to-noise ratio (SNR), the methods perform well. To remedy situations when the methods perform poorly, an accelerometer input is introduced to the formulation. This input is used to predict changes in the frequency for short time intervals.Periodiska signaler förekommer ofta i praktiken. I många tillämpningar är det intressant att försöka skatta frekvensen av dessa periodiska signaler, eller vibrationer, genom mätningar av dem. Detta kallas för frekvensskattning eller frekvensföljning beroende på om frekvensen är konstant eller varierar över tid. Två tillämpningar studeras i denna licentiatavhandling. Målet i båda tillämpningarna är att skatta hastigheten på fordon. Den första tillämpningen handlar om att följa frekvensen av ett fordons motorljud, när fordonet kör genom ett område där mikrofoner har blivit utplacerade. Man kan skatta ett fordons hastighet från motorljudet, vars frekvens beror på Dopplereffekten. Denna avhandling undersöker förbättrad följning av denna frekvens, vilket förbättrar skattningen av hastigheten. Två olika sätt för frekvensföljning används. Ett sätt är att anta att frekvensen är konstant inom korta tidsintervall och räkna ut en skattning av frekvensen. Ett annat sätt är att använda en matematisk modell som tar hänsyn till att frekvensen varierar över tid, och försöka följa den. För detta syfte föreslås det Rao-Blackwelliserade punktmassefiltret. Det är en metod som utnyttjar strukturen i den matematiska modellen av problemet för att erhålla bra prestanda och lägre krav på beräkningskraft. Resultaten visar att den föreslagna metoden förbättrar träffsäkerheten på frekvensföljningen i vissa fall, vilket kan förbättra prestanda för hastighetsskattningen. Den andra tillämpningen handlar om att skatta ett fordons hastighet med enbart en accelerometer (mätare av acceleration) fastsatt i chassit. Hjulvibrationer kan mätas av denna accelerometer. Frekvenserna av dessa vibrationer ges av hjulaxelns rotationshastighet. Om hjulradien är känd eller skattad så kan man räkna ut fordonets hastighet, så att man inte behöver använda externa mätningar som gps eller hjulhastighetsmätningar. Accelerationsmätningarna är brusiga och innehåller outliers, vilka är mätvärden som ibland slumpmässigt kraftigt skiljer sig från det förväntade. Därför studeras metoder som är konstruerade för att hantera dessa. Det föreslås en approximation till Rao-Blackwellisering för att kunna hantera dessa outliers. Det föreslås också en ny frekvensskattningsmetod baserad på expectation-maximization, vilket är ytterligare en metod som utnyttjar strukturer i matematiska modeller. En simuleringsstudie visar att metoderna har lägre genomsnittligt skattningsfel än standardmetoder. På insamlad experimentell data visas att metoderna ofta fungerar, men att de behöver kompletteras med en ytterligare komponent för död räkning (prognosvärden) med accelerometer för att öka antalet testfall där de erhåller godtagbar prestanda.
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Bourkane, Abderrahim. "Estimation du rapport signal à bruit d'un signal GPS par filtrage non linéaire." Thesis, Littoral, 2015. http://www.theses.fr/2015DUNK0384/document.
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Un signal GPS est modulé par une porteuse et est étalé par un code pseudo aléatoire. Sa puissance, qui est portée en dessous du niveau du bruit, ne peut pas être directement mesurée. Les estimateurs classiques de la littérature utilisent les paramètres statistiques du maximum de la corrélation, obtenus après le désétalement du signal pour mesurer la puissance du signal reçu. Ces estimateurs nécessitent une longue période d'intégration pour être précis. De plus, ils ne tiennent pas compte de l'effet de la fréquence Doppler et du nombre de satellites visibles sur la statistique du maximum de la corrélation. Ces effets perturbateurs faussent l'estimation de la valeur C/N0 et limitent les applications qui utilisent cette grandeur telle que la réflectométrie des signaux GNSS. Ce travail de thèse propose un estimateur du rapport signal à bruit propre à chaque satellite, à partir d'un signal GPS L1. Pour présenter cet estimateur, nous avons adopté une approche en deux étapes. On suppose dans la première étape que le signal GPS est numérisé sur 1 bit, et on établit une fonction reliant l'amplitude du signal reçu au maximum de corrélation. Cette fonction non linéaire est déduite de l'architecture radio du récepteur GPS et des paramètres du signal qui sont : la fréquence Doppler et le déphasage du signal reçu. En effet, le rapport signal à bruit est une mesure relative, et pour pouvoir estimer l'amplitude du signal, on suppose que le bruit est blanc, gaussien, centré et de variance unitaire. La fonction proposée étant fortement non linéaire, nous proposons dans une deuxième étape, un estimateur dynamique de l'amplitude du signal, qui utilise le filtrage d'état non linéaire et les observations du maximum de la corrélation. Deux filtres sont évalués à cet effet ; le friltrage de Kalman sans parfum et le filtrage particulaireA gps signal es modulated by a carrier and is spreaded by a pseudo random code. Its power, which is carried below the level of noise, can't be directly measured. Conventional estimators literature using the statistical parameters of the maximum of the correlation, obtained after despreading of the signal to measure the received signal strength. These estimators require a long period of integration to be precise. Moreover, they do not take into account the effect of the Doppler frequency and the number of visible satellites on the statistical maximum of the correlation. These disruptive effects falsify the estimated value of C/N0 and limit the applications of the reflectometry. This thesis proposes an estimator of the signal to noise ratio own to each satellite, from a GPS L1 signal. To present this estimator, we have adopted a two-step approach. it is assumed in the first stage that the GPS signal is digitized on 1 bit, and sets a function relating the amplitude of the signal received to maximum correlation knowing the parameters of the GPS signal which are : the Doppler frequency and the phase shift of the received signal. indeed, the signal to noise ratio is a relative measure, and to estimate the signal amplitude is assumed that the noise is white, Gaussian, centered and unit variance. The proposed function is highly non-linear. We propose in a second step a dynamic estimator of the signal amplitude, which uses the non-linear state filter and the observations of the maximum correlation. Two filters are assessed in this case the Unscented Kalman filter and a particle filter
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Landqvist, Ronnie. "Signal processing techniques in mobile communication systems : signal separation, channel estimation and equalization /." Karlskrona : Blekinge Institute of Technology, 2005. http://www.bth.se/fou/Forskinfo.nsf/allfirst2/98bf8bfb44d67d86c1257099003e2fc1?OpenDocument.
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Andersson, Tomas. "Parameter Estimation and Waveform Fitting for Narrowband Signals." Doctoral thesis, KTH, Skolan för elektro- och systemteknik (EES), 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-235.
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Frequency estimation has been studied for a large number of years. One reason for this is that the problem is easy to understand, but difficult to solve. Another reason, for sure, is the large number of applications that involve frequency estimation, e.g radar using frequency modulated continuous wave (FMCW) techniques where the distance to the target is embedded in the frequency, resonance sensor systems where the output signal is given as the frequency displacement from a nominal frequency, radio frequency identification systems (RFID) where frequency modulation is used in the communication link, etc. The requirement on the frequency estimator varies with the application and typical issues include: accuracy, precision or (bias) processing speed or complexity, and ability to handle multiple signals. A lot of solutions to different problems in this area has been proposed, but still several open questions remain. The first part of this thesis addresses the problem of frequency estimation using low complexity algorithms. One way of achieving such an algorithm is to employ a coarse quantization on the input signal. In this thesis, a 1-bit quantizer is considered which enables the use of low complexity algorithms. Frequency estimation using look-up tables is studied and the properties of such an estimator are presented. By analyzing the look-up tables using the Hadamard transform a novel type of lowcomplexity frequency estimators is proposed. They use operations such as binary multiplication and addition of precalculated constants. This fact makes them suitable in applications where low complexity and high speed are major issues. A hardware demonstrator using the table look-up technique is designed and a prototype is analysed by real measurements. Today, the interest of using digital signal processing instead of analog processing is almost absolute. For example, in testing analog-to-digital converters an important part is to fit a sinewave to the recorded data, as well as to calculate the parameters that in least-squares sense result in the best fit. In this thesis, the sinewave fitting method included in the IEEE Standard 1057 is studied in some detail. Asymptotic Cramér-Rao bounds for three- and four model parameters are derived under the Gaussian assumption. Further, the sinewave fitting properties of the algorithm are analyzed by the parsimony principle. A novel model order selection criterion is proposed for waveform fitting methods in the case of a linear signal model. A generalization of this criterion is made to include the non-linear sinewave fitting application. For multiple sinewave fitting applications two iterative algorithms are proposed. The first method is a combination of the standardized sinewave fit algorithm and the expectation maximization algorithm. The second algorithm is an extension of a single sinewave model to a multiple sinewave model employing the standardized sinewave fitting algorithm. Both algorithms are analysed by numerical means and are shown to accurately resolve multiple sinewaves and produce efficient estimates. Initialization issues of such algorithms are included to some extent.QC 20100830
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Ma, Jun. "Channel estimation and signal detection for wireless relay." Diss., Georgia Institute of Technology, 2010. http://hdl.handle.net/1853/37082.
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Wireless relay can be utilized to extend signal coverage, achieve spatial diversity by user cooperation, or shield mobile terminals from adverse channel conditions over the direct link. In a two-hop multi-input-multi-output (MIMO) amplify-and-forward (AF) relay system, the overall noise at the destination station (DS) consists of the colored noise forwarded from the relay station (RS) and the local white noise. We propose blind noise correlation estimation at the DS by utilizing statistics of the broadband relay channel over the RS-DS hop, which effectively improves signal detection at the DS. For further
performance improvement, we also propose to estimate the two cascaded MIMO relay channels over the source-RS and the RS-DS links at the DS based on the overall channel between the source and the DS and the amplifying matrix applied at the RS. To cancel cross-talk interference at a channel-reuse-relay-station (CRRS), we utilize the random forwarded signals of the CRRS as equivalent pilots for local coupling channel estimation and achieve a much higher post signal-to-interference ratio (SIR) than the conventional
dedicated pilots assisted cancellers without causing any in-band interference at the DS. When an OFDM-based RS is deployed on a high-speed train to shield mobile terminals from the high Doppler frequency over the direct link, inter-subchannel interference (ICI) mitigation is required at the RS. By utilizing statistics of the channel between the base station and the train, we develop both full-rate and reduced-rate OFDM transmission with inherent ICI self-cancellation via transmit and/or receive preprocessing, which achieve significant performance improvement over the existing ICI self-cancellation schemes.
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Zachariah, Dave. "Estimation for Sensor Fusion and Sparse Signal Processing." Doctoral thesis, KTH, Signalbehandling, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-121283.
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Progressive developments in computing and sensor technologies during the past decades have enabled the formulation of increasingly advanced problems in statistical inference and signal processing. The thesis is concerned with statistical estimation methods, and is divided into three parts with focus on two different areas: sensor fusion and sparse signal processing. The first part introduces the well-established Bayesian, Fisherian and least-squares estimation frameworks, and derives new estimators. Specifically, the Bayesian framework is applied in two different classes of estimation problems: scenarios in which (i) the signal covariances themselves are subject to uncertainties, and (ii) distance bounds are used as side information. Applications include localization, tracking and channel estimation. The second part is concerned with the extraction of useful information from multiple sensors by exploiting their joint properties. Two sensor configurations are considered here: (i) a monocular camera and an inertial measurement unit, and (ii) an array of passive receivers. New estimators are developed with applications that include inertial navigation, source localization and multiple waveform estimation. The third part is concerned with signals that have sparse representations. Two problems are considered: (i) spectral estimation of signals with power concentrated to a small number of frequencies,and (ii) estimation of sparse signals that are observed by few samples, including scenarios in which they are linearly underdetermined. New estimators are developed with applications that include spectral analysis, magnetic resonance imaging and array processing.QC 20130426
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Anctil, Benoit. "Signal classification issues in motor unit number estimation." Thesis, McGill University, 2000. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=31043.
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In the electrophysiological evaluation of neuromuscular disorders, the number of active motor units in a muscle is considered one of the most important indicators of the physiological state of peripheral nerve/muscle systems. Electrophysiological techniques have been designed to estimate this number but, despite considerable effort, they only provide an approximation of the exact number. To assist further development in this field, the specific problem of the classification of stimulus-evoked potentials was investigated. Over 1300 series of signals were recorded from four subjects over a five-week period. From these data sets, those having clearly separable unique responses were selected and unsupervised learning methods were employed to identify the different response classes. The lowest misclassification error rate obtained under these conditions was 25%. A subsequent evaluation of an interactive graph-theoretic clustering technique presented interesting results with an error rate of 8%. The findings hold promise for developing better methods to estimate the number of motor units.
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Rau, Christian, and rau@maths anu edu au. "Curve Estimation and Signal Discrimination in Spatial Problems." The Australian National University. School of Mathematical Sciences, 2003. http://thesis.anu.edu.au./public/adt-ANU20031215.163519.
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In many instances arising prominently, but not exclusively, in imaging problems, it is important to condense the salient information so as to obtain a low-dimensional approximant of the data. This thesis is concerned with two basic situations which call for such a dimension reduction. The first of these is the statistical recovery of smooth edges in regression and density surfaces. The edges are understood to be contiguous curves, although they are allowed to meander almost arbitrarily through the plane, and may even split at a finite number of points to yield an edge graph. A novel locally-parametric nonparametric method is proposed which enjoys the benefit of being relatively easy to implement via a `tracking' approach. These topics are discussed in Chapters 2 and 3, with pertaining background material being given in the Appendix. In Chapter 4 we construct concomitant confidence bands for this estimator, which have asymptotically correct coverage probability. The construction can be
likened to only a few existing approaches, and may thus be considered as our main contribution.
¶
Chapter 5 discusses numerical issues pertaining to the edge and confidence band estimators of Chapters 2-4. Connections are drawn to popular topics which originated in the fields of computer vision and signal processing, and which surround edge detection. These connections are exploited so as to obtain greater robustness of the likelihood estimator, such as with the presence of sharp corners.
¶
Chapter 6 addresses a dimension reduction problem for spatial data where the ultimate objective of the analysis is the discrimination of these data into one of a few pre-specified groups. In the dimension reduction step, an instrumental role is played by the recently
developed methodology of functional data analysis. Relatively standar non-linear image processing techniques, as well as wavelet shrinkage, are used prior to this step. A case study for remotely-sensed navigation radar data exemplifies the methodology of Chapter 6.
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Maca, Gregory A. "Array signal parameter estimation for CDMA technology (ASPECT)." Ann Arbor, Mich. : ProQuest, 2007. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:3245932.
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Thesis (Ph.D. in Electrical Engineering)--S.M.U., 2007.Title from PDF title page (viewed Mar. 18, 2008). Source: Dissertation Abstracts International, Volume: 67-12, Section: B, page: 7272. Adviser: Mandyam Srinath. Includes bibliographical references.
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Rau, Christian. "Curve estimation and signal discrimination in spatial problems /." View thesis entry in Australian Digital Theses Program, 2003. http://thesis.anu.edu.au/public/adt-ANU20031215.163519/index.html.
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Spence, G. S. "A joint estimation approach for periodic signal analysis." Thesis, Queen's University Belfast, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.396587.
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Örneskans, Alexander. "Signal-filtration methodology for estimation of fuel level." Thesis, Luleå tekniska universitet, Institutionen för system- och rymdteknik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-70071.
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Knowing how much fuel there is in a car is important for a predictable driving experience. Such knowledge will dictate how a person drives, when to refuel and how long they can drive. However unprocessed fuel level signals are highly noisy and therefore misleading.To ensure a good and predictable driving experience it is important to estimate the fuel level. The way this thesis has tackled this problem is by comparing and evaluating different filtering methods.The estimation algorithms were designed based on a saddle type tank developed by Volvo Car Corporation. The fuel level sensor consists of a floater arm and can only detect fuel levels within its maximal and minimal positions. The tank size can deviate from the standard volume and it will affect the measurement. Acceleration, angular orientation and fuel consumption are all factors that disturb fuel level estimation and therefore their relationship to the estimation problem is investigated. An experiment was devised to investigate the relationship between angular orientation, fuel volume and fuel level readings. ARX based models were made including angular orientation or acceleration. The relationship was concluded to be non-linear. The Kalman, $H_{\infty}$, Particle and Recursive Least Squares filters were compared. The Kalman and RLS filters had the most desirable traits and were therefore further developed. Both Kalman and RLS resulted in smooth estimates on the driving cycles tested.The Kalman filter provided a steadier estimate and could be easily tuned for faster convergence to zero. The Kalman filter can easily be changed to accommodate parametric uncertainties which improve its robust qualities.However the relationship between angular orientation and fuel level readings are non-linear. Therefore the RLS method was considered more robust for a reduced biased fuel reading under angular orientations. In conclusion the most desirable filter is a filter that provides the best traits from both filters.
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He, Bing. "Estimation paramétrique du signal par réseaux de neurones." Lille 1, 2002. https://pepite-depot.univ-lille.fr/RESTREINT/Th_Num/2002/50376-2002-75.pdf.
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Cheng, ChienChun. "MIMO signal design, channel estimation, and symbol detection." Thesis, Université Paris-Saclay (ComUE), 2016. http://www.theses.fr/2016SACLC003/document.
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Cette thèse aborde plusieurs problèmes fondamentaux des systèmes de communications sans fil avec des antennes multiples, dites systèmes MIMO (multiple input, multiple output). Les contributions se situent aussi bien au niveau des algorithmes de réception qu’au niveau de la génération du signal à l’émission.La plus grande partie de la thèse est dédiée à l’étude des algorithmes de réception. Les points abordés comprennent la modélisation et l’estimation du canal, la détection robuste des symboles, et la suppression des interférences. Un nouveau modèle de canal est proposé dans le chapitre 3 en exploitant les corrélations dans les domaines temporel, fréquentiel et spatial, et en réduisant l’espace des paramètres aux termes dominants. Ce modèle est utilisé pour proposer ensuite un estimateur de canal à faible complexité et aussi un sélecteur de mots de code pour envoyer vers l’émetteur les informations sur l’état du canal. Dans le chapitre 4, la réception robuste est étudiée pour les systèmes MIMO-OFDM sans une connaissance parfaite du canal. Des récepteurs robustes sont proposés pour les cas avec ou sans connaissance statistique du canal. La conception de récepteurs pour les systèmes MIMO-OFDM en présence d’interférence est étudiée dans le chapitre 5 et des récepteurs robustes sont proposés prenant en compte séparément l’interférence causée par les ondes pilotes et celle causée par les symboles d’une part et l’asynchronisme entre le signal et l’interférence d’autre part.Dans la deuxième partie de la thèse (chapitre 6), nous abordons les modulations spatiales qui sont particulièrement adaptées aux systèmes MIMO dans lesquels le nombre de chaines d’émission est inférieur aux nombre d’antennes. Remarquant que l’efficacité spectrale de ces systèmes reste très faible par rapport à la technique de multiplexage spatiale, nous avons développé des modulations spatiales améliorées (ESM, pour Enhanced Spatial Modulation) qui augmentent substantiellement l’efficacité spectrale. Ces modulations sont basées sur l’introduction de modulations secondaires, obtenues par interpolation. La technique ESM gagne plusieurs décibels en rapport signal à bruit lorsque les constellations du signal sont choisies de façon à avoir la même efficacité spectrale que dans les modulations spatiales conventionnellesThe aim of this thesis is to investigate multiple input multiple output (MIMO) techniques from the reception algorithms, i.e., channel estimation, symbol detection, and interference suppression, to the advanced spatial modulation (SM) transmission schemes, i.e., the signal constellation design for high performance and energy efficiency. In the reception algorithms, the proposed schemes are derived based on the detection theory, i.e., maximum likelihood (ML), linear minimum mean square error (MMSE), successive interference cancellation (SIC), combining with the statistical analysis, i.e., Bayesian linear regression and Bayesian model comparison, in order to deal with the channel uncertainty, i.e., fading, correlations, thermal noise, multiple interference, and the impact of estimation errors.In the transmission schemes, the signal constellations are targeted to find a good trade off between the average transmit energy and the minimum Euclidean distance in the signal space. The proposed schemes, denoted by enhanced SM (ESM), introduce novel modulation/antenna combinations and use them as the information bits for transmission. The number of those combinations is the double or the quadruple of the number of active antenna indices (or index combinations) in conventional SM systems, and this increases the number of bits transmitted per channel use by one or two.The results of simulations show that good system performance can be achieved with the advanced MIMO techniques. Several examples are presented in this thesis to provide insights for the MIMO system designs
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Mao, Xiaolei. "GPS CARRIER SIGNAL PARAMETERS ESTIMATION UNDER IONOSPHERE SCINTILLATION." Miami University / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=miami1314295002.
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Ren, Mengqi. "JOINT DETECTION-STATE ESTIMATION AND SECURE SIGNAL PROCESSING." VCU Scholars Compass, 2016. http://scholarscompass.vcu.edu/etd/4662.
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In this dissertation, joint detection-state estimation and secure signal processing are studied. Detection and state estimation are two important research topics in surveillance systems. The detection problems investigated in this dissertation include object detection and fault detection. The goal of object detection is to determine the presence or absence of an object under measurement uncertainty. The aim of fault detection is to determine whether or not the measurements are provided by faulty sensors. State estimation is to estimate the states of moving objects from measurements with random measurement noise or disturbance, which typically consist of their positions and velocities over time. Detection and state estimation are typically implemented separately and state estimation is usually performed after the decision is made. In this two-stage approach, missed detection and false alarms in detection stage decrease accuracy of state estimation. In this dissertation, several joint detection and state estimation algorithms are proposed. Secure signal processing is indispensable in dynamic systems especially when an adversary exists. In this dissertation, the developed joint fault detection and state estimation approach is used to detect attacks launched by an adversary on the system and improve state estimation accuracy. The security problem in satellite communication systems is studied and a minimax anti-jammer is designed in a frequency hopping spread spectrum (FHSS)/quadrature phase-shift keying (QPSK) satellite communication system.
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Forsling, Robin. "Decentralized Estimation Using Conservative Information Extraction." Licentiate thesis, Linköpings universitet, Reglerteknik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-171998.
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Sensor networks consist of sensors (e.g., radar and cameras) and processing units (e.g., estimators), where in the former information extraction occurs and in the latter estimates are formed. In decentralized estimation information extracted by sensors has been pre-processed at an intermediate processing unit prior to arriving at an estimator. Pre-processing of information allows for the complexity of large systems and systems-of-systems to be significantly reduced, and also makes the sensor network robust and flexible. One of the main disadvantages of pre-processing information is that information becomes correlated. These correlations, if not handled carefully, potentially lead to underestimated uncertainties about the calculated estimates. In conservative estimation the unknown correlations are handled by ensuring that the uncertainty about an estimate is not underestimated. If this is ensured the estimate is said to be conservative. Neglecting correlations means information is double counted which in worst case implies diverging estimates with fatal consequences. While ensuring conservative estimates is the main goal, it is desirable for a conservative estimator, as for any estimator, to provide an error covariance which is as small as possible. Application areas where conservative estimation is relevant are setups where multiple agents cooperate to accomplish a common objective, e.g., target tracking, surveillance and air policing. The first part of this thesis deals with theoretical matters where the conservative linear unbiased estimation problem is formalized. This part proposes an extension of classical linear estimation theory to the conservative estimation problem. The conservative linear unbiased estimator (CLUE) is suggested as a robust and practical alternative for estimation problems where the correlations are unknown. Optimality criteria for the CLUE are provided and further investigated. It is shown that finding an optimal CLUE is more complicated than finding an optimal linear unbiased estimator in the classical version of the problem. To simplify the problem, a CLUE that is optimal under certain restrictions will also be investigated. The latter is named restricted best CLUE. An important result is a theorem that gives a closed form solution to a restricted best CLUE. Furthermore, several conservative estimation methods are described followed by an analysis of their properties. The methods are shown to be conservative and optimal under different assumptions about the underlying correlations. The second part of the thesis focuses on practical aspects of the conservative approach to decentralized estimation in configurations where the communication channel is constrained. The diagonal covariance approximation is proposed as a data reduction technique that complies with the communication constraints and if handled correctly can be shown to preserve conservative estimates. Several information selection methods are derived that can reduce the amount of data being transmitted in the communication channel. Using the information selection methods it is possible to decide what information other actors of the sensor network find useful.
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Fu, Ming Fai. "Motion estimation and compensation in wavelet domain and fast global motion estimation for video coding /." View Abstract or Full-Text, 2002. http://library.ust.hk/cgi/db/thesis.pl?ELEC%202002%20FU.
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Thesis (M. Phil.)--Hong Kong University of Science and Technology, 2002.Includes bibliographical references (leaves 98-102). Also available in electronic version. Access restricted to campus users.
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Björk, Marcus. "Contributions to Signal Processing for MRI." Doctoral thesis, Uppsala universitet, Avdelningen för systemteknik, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-246537.
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Magnetic Resonance Imaging (MRI) is an important diagnostic tool for imaging soft tissue without the use of ionizing radiation. Moreover, through advanced signal processing, MRI can provide more than just anatomical information, such as estimates of tissue-specific physical properties. Signal processing lies at the very core of the MRI process, which involves input design, information encoding, image reconstruction, and advanced filtering. Based on signal modeling and estimation, it is possible to further improve the images, reduce artifacts, mitigate noise, and obtain quantitative tissue information. In quantitative MRI, different physical quantities are estimated from a set of collected images. The optimization problems solved are typically nonlinear, and require intelligent and application-specific algorithms to avoid suboptimal local minima. This thesis presents several methods for efficiently solving different parameter estimation problems in MRI, such as multi-component T2 relaxometry, temporal phase correction of complex-valued data, and minimizing banding artifacts due to field inhomogeneity. The performance of the proposed algorithms is evaluated using both simulation and in-vivo data. The results show improvements over previous approaches, while maintaining a relatively low computational complexity. Using new and improved estimation methods enables better tissue characterization and diagnosis. Furthermore, a sequence design problem is treated, where the radio-frequency excitation is optimized to minimize image artifacts when using amplifiers of limited quality. In turn, obtaining higher fidelity images enables improved diagnosis, and can increase the estimation accuracy in quantitative MRI.
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Sakarya, Fatma Ayhan. "Passive source location estimation." Diss., Georgia Institute of Technology, 1992. http://hdl.handle.net/1853/13714.
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Whitaker, Meredith Kathryn. "Estimating Signal Features from Noisy Images with Stochastic Backgrounds." Diss., The University of Arizona, 2008. http://hdl.handle.net/10150/195144.
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Imaging is often used in scientific applications as a measurement tool. The location of a target, brightness of a star, and size of a tumor are all examples of object features that are sought after in various imaging applications. A perfect measurement of these quantities from image data is impossible because of, most notably, detector noise fluctuations, finite resolution, sensitivity of the imaging instrument, and obscuration by undesirable object structures. For these reasons, sophisticated image-processing techniques are designed to treat images as random variables. Quantities calculated from an image are subject to error and fluctuation; implied by calling them estimates of object features.This research focuses on estimator error for tasks common to imaging applications. Computer simulations of imaging systems are employed to compare the estimates to the true values. These computations allow for algorithm performance tests and subsequent development. Estimating the location, size, and strength of a signal embedded in a background structure from noisy image data is the basic task of interest. The estimation task's degree of difficulty is adjusted to discover the simplest data-processing necessary to yield successful estimates.Even when using an idealized imaging model, linear Wiener estimation was found to be insufficient for estimating signal location and shape. These results motivated the investigation of more complex data processing. A new method (named the scanning-linear estimator because it maximizes a linear functional) is successful in cases where linear estimation fails. This method has also demonstrated positive results when tested in realistic simulations of tomographic SPECT imaging systems. A comparison to a model of current clinical estimation practices found that the scanning-linear method offers substantial gains in performance.
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Boman, Katarina. "Low-angle estimation : Models, methods and bounds." Licentiate thesis, Uppsala universitet, Avdelningen för systemteknik, 2000. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-85998.
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In this work we study the performance of elevation estimators and lower bounds on the estimation error variance for a low angle target in a smooth sea scenario using an array antenna. The article is structured around some key assumptions on multipath knowledge, signal parameterization and noise covariance, giving the reader a framework in which Maximum Likelihood estimators exploiting different á priori information can be found. The crucial factor that determines the estimator accuracy is the multipath modeling, and there are three alternative levels of knowledge that can be used: 1) two unknown target locations 2) the target and its corresponding sea-reflection are related via simple geometry 3) the sea-reflection coefficient is known as a function of grazing angle. A compact expression for the Cramér–Rao lower bound is derived, including all special cases of the key assumptions. We prove that the Cramér–Rao bound is highly dependent on the multipath model, while it is the same for the different signal parameterizations and that it is independent of the noise covariance. However, the Cramér–Rao bound is sometimes too optimistic and not achievable. The tighter Barankin bound is derived to predict the threshold behavior seen at low SNR. At high SNR the Barankin bound coincides with the Cramér–Rao bound. Simulations show that the Maximum Likelihood methods are statistically efficient and achieve the theoretical lower bound on error variance, in case of high enough SNR. The bounds are also useful tools to design an improved array structure that can give better performance than the standard uniform linear array structure. The influence of the number of sensors and the number of snapshots on the error variance is also studied, showing the rate of improvement with more sensors or snapshots. Finally we discuss the use of multiple frequencies, which is mainly a tool for suppressing ambiguities. We show for which signal models it provides improved performance.
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Papadopoulos, Hélène. "Estimation conjointe d'information de contenu musical d'un signal audio." Phd thesis, Université Pierre et Marie Curie - Paris VI, 2010. http://tel.archives-ouvertes.fr/tel-00548952.
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Depuis quelques années, nous assistons à l'augmentation croissante de gigantesques collections de musique en ligne. Ce phénomène a attiré l'attention de nombreux chercheurs. En effet, le besoin urgent de développer des outils et des méthodes qui permettent d'interagir avec ces énormes bibliothèques de musique numérique pose des défis scientifiques complexes. Le domaine de la recherche d'information musicale (Music Information Retrieval, MIR) est ainsi devenu très actif depuis une dizaine d'années. Ce domaine général inclut celui de l'indexation musicale dans lequel s'inscrit cette thèse qui a pour but d'aider au stockage, à la diffusion et la consultation des gigantesques collections de musique en ligne. Ce domaine ouvre de nombreuses perspectives pour l'industrie et la recherche liées aux activités multimédia. Dans cette thèse, nous nous intéressons au problème de l'extraction automatique d'informations de contenu d'un signal audio de musique. La plupart des travaux existants abordent ce problème en considérant les attributs musicaux de manière indépendante les uns vis-à-vis des autres. Cependant les morceaux de musique sont extrèmement structurés du point de vue de l'harmonie et du rythme et leur estimation devrait se faire en tenant compte du contexte musical, comme le fait un musicien lorsqu'il analyse un morceau de musique. Nous nous concentrons sur trois descripteurs musicaux liés aux structures harmoniques, métriques et tonales d'un morceau de musique. Plus précisément, nous cherchons à en estimer la progression des accords, les premiers temps et la tonalité. L'originalité de notre travail consiste à construire un modèle qui permet d'estimer de manière conjointe ces trois attributs musicaux. Notre objectif est de montrer que l'estimation des divers descripteurs musicaux est meilleure si on tient compte de leurs dépendances mutuelles que si on les estime de manière indépendante. Nous proposons au cours de ce travail un ensemble de protocoles de comparaison, de métriques de performances et de nouvelles bases de données de test afin de pouvoir évaluer les différentes méthodes étudiées. Afin de valider notre approche, nous présentons également les résultats de nos participations à des campagnes d'évaluation internationales. Dans un premier temps, nous examinons plusieurs représentations typiques du signal audio afin de choisir celle qui est la plus appropriée à l'analyse du contenu harmonique d'un morceau de musique. Nous explorons plusieurs méthodes qui permettent d'extraire un chromagram du signal et les comparons à travers un protocole d'évaluation original et une nouvelle base de données que nous avons annotée. Nous détaillons et expliquons les raisons qui nous ont amenés à choisir la représentation que nous utilisons dans notre modèle. Dans notre modèle, les accords sont considérés comme un attribut central autour duquel les autres descripteurs musicaux s'organisent. Nous étudions le problème de l'estimation automatique de la suite des accords d'un morceau de musique audio en utilisant les _chromas_ comme observations du signal. Nous proposons plusieurs méthodes basées sur les modèles de Markov cachés (hidden Markov models, HMM), qui permettent de prendre en compte des éléments de la théorie musicale, le résultat d'expériences cognitives sur la perception de la tonalité et l'effet des harmoniques des notes de musique. Les différentes méthodes sont évaluées et comparées pour la première fois sur une grande base de données composée de morceaux de musique populaire. Nous présentons ensuite une nouvelle approche qui permet d'estimer de manière simultanée la progression des accords et les premiers temps d'un signal audio de musique. Pour cela, nous proposons une topologie spécifique de HMM qui nous permet de modéliser la dépendance des accords par rapport à la structure métrique d'un morceau. Une importante contribution est que notre modèle peut être utilisé pour des structures métriques complexes présentant par exemple l'insertion ou l'omission d'un temps, ou des changements dans la signature rythmique. Le modèle proposé est évalué sur un grand nombre de morceaux de musique populaire qui présentent des structures métriques variées. Nous comparons les résultats d'un modèle semi-automatique, dans lequel nous utilisons les positions des temps annotées manuellement, avec ceux obtenus par un modèle entièrement automatique où la position des temps est estimée directement à partir du signal. Enfin, nous nous penchons sur la question de la tonalité. Nous commençons par nous intéresser au problème de l'estimation de la tonalité principale d'un morceau de musique. Nous étendons le modèle présenté ci-dessus à un modèle qui permet d'estimer simultanément la progression des accords, les premiers temps et la tonalité principale. Les performances du modèle sont évaluées à travers des exemples choisis dans la musique populaire. Nous nous tournons ensuite vers le problème plus complexe de l'estimation de la tonalité locale d'un morceau de musique. Nous proposons d'aborder ce problème en combinant et en étendant plusieurs approches existantes pour l'estimation de la tonalité principale. La spécificité de notre approche est que nous considérons la dépendance de la tonalité locale par rapport aux structures harmonique et métrique. Nous évaluons les résultats de notre modèle sur une base de données originale composée de morceaux de musique classique que nous avons annotés.
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Ozbek, Ibrahim Yucel. "Dynamic System Modeling And State Estimation For Speech Signal." Phd thesis, METU, 2010. http://etd.lib.metu.edu.tr/upload/3/12611777/index.pdf.
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This thesis presents an all-inclusive framework on how the current formant tracking and audio (and/or visual)-to-articulatory inversion algorithms can be improved.
The possible improvements are summarized as follows:
The first part of the thesis investigates the problem of the formant frequency estimation when the number of formants to be estimated fixed or variable respectively.
The fixed number of formant tracking method is based on the assumption that the number of formant frequencies is fixed along the speech utterance. The proposed algorithm is based on the combination of
a dynamic programming algorithm and Kalman filtering/smoothing. In this method, the speech signal is divided into voiced and unvoiced segments, and the formant candidates are associated via dynamic programming algorithm for each voiced and unvoiced part separately. Individual adaptive Kalman filtering/smoothing is used to perform the formant frequency estimation. The performance of the proposed algorithm is compared with some algorithms given in the literature.
The variable number of formant tracking method considers those formant frequencies which are visible in the spectrogram. Therefore, the number of formant frequencies is not fixed and they can change along the speech waveform. In that case, it is also necessary to estimate the number of formants to track. For this purpose, the proposed algorithm uses extra logic (formant track start/end decision unit). The measurement update of each individual formant trajectories is handled via Kalman filters. The performance of the proposed algorithm is illustrated by some examples
The second part of this thesis is concerned with improving audiovisual to articulatory inversion performance. The related studies can be examined in two partsGaussian mixture model (GMM) regression based inversion and Jump Markov Linear System (JMLS) based inversion. GMM regression based inversion method involves modeling audio (and /or visual) and articulatory data as a joint Gaussian mixture model. The conditional expectation of this distribution gives the desired articulatory estimate. In this method, we examine the usefulness of the combination of various acoustic features and effectiveness of various types of fusion techniques in combination with audiovisual features. Also, we propose dynamic smoothing methods to smooth articulatory trajectories. The performance of the proposed algorithm is illustrated and compared with conventional algorithms. JMLS inversion involves tying the acoustic (and/or visual) spaces and articulatory space via multiple state space representations. In this way, the articulatory inversion problem is converted into the state estimation problem where the audiovisual data are considered as measurements and articulatory positions are state variables. The proposed inversion method first learns the parameter set of the state space model via an expectation maximization (EM) based algorithm and the state estimation is handled via interactive multiple model (IMM) filter/smoother.
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Gami, Hirenkumar. "Signal parameter estimation methods: The non-eigenvector based approach." Diss., Wichita State University, 2009. http://hdl.handle.net/10057/2552.
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An idea behind this dissertation is the estimation of signal parameters of a radio channel
snapshot. The focal point here to utilize the high resolution estimation capabilities of subspace
based methods in association with non-eigenvector based parameter estimation methods to
reduce the complexity in wireless system parameter estimation process.
The first part of the work is to scrutinize various subspace based parametric estimation
methods in well explored array signal processing based wireless communication system problem.
These high resolution spectral parameter estimation methods broadly classified in terms of
eigenvector based and non-eigenvector based estimation methods. Although providing high
resolution and well-known in literature, the eigenvector based spectral parameter estimation
methods do not comply requirements of real time signal processing of today's highly complex
radio receivers. Therefore, this dissertation is focusing on computationally efficient noneigenvector
methods for signal spectral parameter estimation such as Rank Revealing QR
factorization, Propagator Method, Accelerated MUSIC, and other triangular factorization
methods.
The second part of this dissertation concentrate on performance evaluation of these noneigenvector
based methods in real world communication system problems. The performance of
these methods is demonstrated under three different parameter estimation problems such as
multipath time delay estimation in FH-CDMA system, channel estimation problem in MUMIMO
system, and joint parameter estimation problem in array signal processing. The role of
eigenvector based methods in the spectral parameter estimation is efficiently transformed into
non-eigenvector based parameter estimation procedure. This transformation leads to
development of computationally efficient algorithms with an enhanced estimation capability.Thesis (Ph.D.)--Wichita State University, College of Engineering, Dept. of Electrical Engineering and Computer Science
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Kuhlman, Michael Joseph. "Mixed-Signal Sensing, Estimation and Control for Miniature Robots." Thesis, University of Maryland, College Park, 2013. http://pqdtopen.proquest.com/#viewpdf?dispub=1541601.
Full textAbstract:
Control of miniature mobile robots in unconstrained environments is an ongoing challenge. Miniature robots often exhibit nonlinear dynamics and obstacle avoidance introduces significant complexity in the control problem. In order to allow for coordinated movements, the robots must know their location relative to the other robots; this is challenging for very small robots operating under severe power and size constraints. This drastically reduces on-board digital processing power and suggests the need for a robust, compact distance sensor and a mixed-signal control system using Extended Kalman Filtering and Randomized Receding Horizon Control to support decentralized coordination of autonomous mini-robots. Error analysis of the sensor suggests that system clock timing jitter is the dominant contributor for sensor measurement uncertainty. Techniques for system identification of model parameters and the design of a mixed-signal computer for mobile robot position estimation are presented.
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Rubio, Molina-Prados Francisco E. "Generalized consistent estimation in arbitrarily high dimensional signal processing." Doctoral thesis, Universitat Politècnica de Catalunya, 2008. http://hdl.handle.net/10803/6918.
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La teoría del procesado estadístico de la señal halla un amplio abanico de aplicaciones en los campos de las comunicaciones de datos, así como también en el procesado con agrupaciones de sensores. Ciertamente, un gran número de estas aplicaciones pueden ser interpretadas como un problema de estimación paramétrica, típicamente resuelto mediante una operación de filtrado lineal actuando sobre un conjunto de observaciones multidimensionales. Esta disertación está dedicada al diseño y evaluación de métodos de procesado estadístico de la señal en condiciones de implementación realistas encontradas en la práctica.Las técnicas tradicionales de procesado estadístico de la señal proporcionan un rendimiento satisfactorio dada la disponibilidad de un número particularmente elevado de observaciones de dimensión finita. En efecto, las condiciones de optimalidad originales no pueden garantizarse en teoría a menos que el número de muestras disponibles aumente de forma asintótica.
En base a esta suposición, en ocasiones se puede obtener una caracterización estadística haciendo uso de la teoría de grandes muestras para matrices de covarianza muestral. En la práctica, no obstante, la aplicación de estos métodos debe necesariamente basarse en una ventana de observación de longitud finita. Además, la dimensión de las muestras recibidas, y el tamaño de la ventana de observación son a menudo comparables en magnitud. En estas situaciones, los planteamientos basados en el análisis estadístico multivariante clásico pierden eficiencia de forma significativa.
En esta tesis se proporciona un marco teórico para la caracterización de la pérdida de eficiencia que los enfoques estadísticos clásicos experimentan en aplicaciones típicas del procesado de la señal en las condiciones prácticas mencionadas con anterioridad. En base a la teoría del análisis espectral de matrices aleatorias de grandes dimensiones, o teoría de matrices aleatorias, se construye una familia de métodos de inferencia estadística que superan las limitaciones de los esquemas de estimación tradicionales para un tamaño de muestra y dimensión de la observación comparativamente grandes. Específicamente, los estimadores de la nueva clase obtenida generalizan las implementaciones al uso siendo consistentes incluso para observaciones con dimensión arbitrariamente grande.
En particular, el marco teórico propuesto es empleado para caracterizar de forma adecuada el rendimiento de sistemas multi-antena con preámbulos de entrenamiento en un régimen asintótico más acorde definido por un tamaño y dimensión de las muestras que crecen sin límite con razón constante. Además, el problema de filtrado óptimo de rango reducido es revisado y extendido de forma que se satisfaga la definición anterior de consistencia generalizada. Por otro parte, se proporciona una caracterización asintótica en el doble límite de un conjunto de formas cuadráticas de las potencias negativas de la covarianza de la observación que generaliza los resultados existentes referentes a los momentos negativos de la distribución de Wishart. A partir de estos resultados, se construye una clase de estimadores de potencia de fuente mejorados que son robustos a imprecisiones en el conocimiento del nivel de ruido y de la matriz de covarianza real.
Con el propósito de reducir la complejidad computacional asociada a implementaciones prácticas basadas en la inversión de matrices, se aborda una solución a los problemas anteriores en términos de las potencias positivas de la matriz de covarianza muestral. A tal efecto, se obtienen una clase de estimadores consistentes generalizados del espectro de la matriz de covarianza y del nivel de potencia en el subespacio de Krylov definido por la covarianza real y el vector de firma asociado al parámetro de interés. Como contribución final, se propone una arquitectura de filtrado robusto a constricciones de la firma que es consistente en el régimen doblemente asintótico de referencia a lo largo de la tesis.
The theory of statistical signal processing finds a wide variety of applications in the fields of data communications, such as in channel estimation, equalization and symbol detection, and sensor array processing, as in beamforming, and radar and sonar systems. Indeed, a large number of these applications can be interpreted in terms of a parametric estimation problem, typically approached by a linear filtering operation acting upon a set of multidimensional observations. This dissertation is devoted to the design and evaluation of statistical signal processing methods under realistic implementation conditions encountered in practice.
Traditional statistical signal processing techniques intrinsically provide a good performance under the availability of a particularly high number of observations of fixed dimension. Indeed, the original optimality conditions cannot be theoretically guaranteed unless the number of samples increases asymptotically to infinity. In practice, though, the application of these methods to the implementation of practical signal processing systems must rely on an observation window of finite length. Moreover, the dimension of the received samples and the window size are most often comparable in magnitude. Under these situations, approaches based on the classical multivariate statistical analysis significantly lose efficiency or cannot even be applied. As a consequence, the performance of practical solutions in some real situations might turn out to be unacceptable.
In this dissertation, a theoretical framework for characterizing the efficiency loss incurred by classical multivariate statistical approaches in conventional signal processing applications under the practical conditions mentioned above is provided. Based on the theory of the spectral analysis of large-dimensional random matrices, or random matrix theory (RMT), a family of new statistical inference methods overcoming the limitations of traditional inferential schemes under comparably large sample-size and observation dimension is derived. Specifically, the new class of consistent estimators generalize conventional implementations by proving to be consistent even for a limited number of samples per filtering degree-of-freedom.
In particular, the proposed theoretical framework is shown to properly characterize the performance of multi-antenna systems with training preambles in the more meaningful asymptotic regime defined by both sample size and dimension increasing without bound at the same rate. Moreover, the problem of optimum reduced-rank linear filtering is reviewed and extended to satisfy the previous generalized consistency definition. On the other hand, an asymptotic characterization of a set of vector-valued quadratic forms involving the negative powers of the observation covariance is provided that generalizes existing results on the limiting eigenvalue moments of the inverse Wishart distribution. Using these results, a new generalized consistent eigenspectrum estimator is derived that uniquely relies on the sample covariance matrix (SCM) and does not require matrix eigendecomposition. The effectiveness of the previous spectral estimator is demonstrated via the construction of a source power estimator that is robust to inaccuracies in the knowledge of both noise level and true covariance matrix.
In order to alleviate the computation complexity issue associated with practical implementations involving matrix inversions, a solution to the two previous problems is afforded in terms of the positive powers of the SCM. To that effect, a class of generalized consistent estimators of the covariance eigenspectrum and the power level are obtained on the Krylov subspace defined by the true covariance matrix and the signature vector associated with the intended parameter. Finally, a signal-mismatch robust filtering architecture is proposed that is consistent in the doubly-asymptotic regime.
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Karasu, Mücahit. "AR parameter estimation using TMS320C30 digital signal processor chip /." Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 1995. http://handle.dtic.mil/100.2/ADA305733.
Full textAbstract:
Thesis (M.S. in Electrical Engineering) Naval Postgraduate School, December 1995.Thesis advisor(s): M.K. Shields, Murali Tummala. "December 1995." Includes bibliographical references. Also available online.
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Costa, João Paulo Carvalho Lustosa da. "Parameter estimation techniques for multi-dimensional array signal processing." Aachen Shaker, 2010. http://d-nb.info/1000960765/04.
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Palmer, Duncan. "Position estimation using the Digital Audio Broadcast (DAB) signal." Thesis, University of Nottingham, 2011. http://eprints.nottingham.ac.uk/12456/.
Full textAbstract:
Over the past decades, there have been a number of trends that have driven the desire to improve the ability to navigate in all environments. While the Global Positioning System has been the driving factor behind most of these trends, there are limitations to this system that have become more evident over time as the world has increasingly come to rely on navigation. These limitations are mostly due to the low transmission power of the satellites, where navigation signals broadcast from space are comparatively weak, especially by the time they have travelled to receivers on the ground. This makes the signals particularly vulnerable to fading in difficult environments such as "urban jungles" and other built up areas. The low signal-to-noise ratio (SNR) also means, that the signals are susceptible to jamming, both hostile and accidental. This motivates the need for alternatives technologies to satellite navigation and consider terrestrial based alternatives such as LORAN-C and eLORAN, but there is also significant interest in the exploitation of other non-navigation signals for positioning and navigation purposes. These so-called 'Signals of Opportunity' do not generally require any alterations to existing communications transmission infrastructure and utilise alternative multi-carrier modulation techniques to those used by navigation systems. This project examines the use of such a signal, the Digital Audio Broadcast (DAB) signal, as a positioning source. This thesis contains complete research from initial coverage simulations in the UK, through to extensive static testing, and the use of the signal in a dynamic environment and it has been shown that the Digital Audio Broadcast signal has potential as a terrestrial based positioning signal.
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Karasu, Mucahit. "AR parameter estimation using TMS320C30 digital signal processor chip." Thesis, Monterey, California. Naval Postgraduate School, 1995. http://hdl.handle.net/10945/31332.
Full textAbstract:
Autoregressive analysis is used in modern signal processing applications for modeling and estimation of random signals. High speed digital signal processors with advanced architecture and special digital signal processing instructions, mostly compiled in C language, can be used in these applications to achieve realtime performance. A commercially available digital signal processor has been used in this work to estimate the AR parameters and power spectral density from the given input data by using the Levinson, Burg and Schur algorithms. This work produced a library file that contains the object files of the AR parameter estimation algorithms. The time required in terms of the cycle counts to execute each algorithm is listed for different data lengths and model orders.
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Noland, Katy C. "Computational tonality estimation : signal processing and hidden Markov models." Thesis, Queen Mary, University of London, 2009. http://qmro.qmul.ac.uk/xmlui/handle/123456789/8492.
Full textAbstract:
This thesis investigates computational musical tonality estimation from an audio signal. We present a hidden Markov model (HMM) in which relationships between chords and keys are expressed as probabilities of emitting observable chords from a hidden key sequence. The model is tested first using symbolic chord annotations as observations, and gives excellent global key recognition rates on a set of Beatles songs. The initial model is extended for audio input by using an existing chord recognition algorithm, which allows it to be tested on a much larger database. We show that a simple model of the upper partials in the signal improves percentage scores. We also present a variant of the HMM which has a continuous observation probability density, but show that the discrete version gives better performance. Then follows a detailed analysis of the effects on key estimation and computation time of changing the low level signal processing parameters. We find that much of the high frequency information can be omitted without loss of accuracy, and significant computational savings can be made by applying a threshold to the transform kernels. Results show that there is no single ideal set of parameters for all music, but that tuning the parameters can make a difference to accuracy. We discuss methods of evaluating more complex tonal changes than a single global key, and compare a metric that measures similarity to a ground truth to metrics that are rooted in music retrieval. We show that the two measures give different results, and so recommend that the choice of evaluation metric is determined by the intended application. Finally we draw together our conclusions and use them to suggest areas for continuation of this research, in the areas of tonality model development, feature extraction, evaluation methodology, and applications of computational tonality estimation.
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Verbout, Shawn M. (Shawn Matthew). "A framework for non-Gaussian signal modeling and estimation." Thesis, Massachusetts Institute of Technology, 1999. http://hdl.handle.net/1721.1/80145.
Full textAbstract:
Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1999.Includes bibliographical references (p. [235]-240).
by Shawn Matthew Verbout.
Ph.D.
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エディ, タユフェール, and Eddy Taillefer. "Direction of arrival estimation using hexagonal-array signal processing." Thesis, https://doors.doshisha.ac.jp/opac/opac_link/bibid/BB10290094/?lang=0, 2008. https://doors.doshisha.ac.jp/opac/opac_link/bibid/BB10290094/?lang=0.
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