Relevant bibliographies by topics / Signal estimation

Academic literature on the topic 'Signal estimation'

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Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Signal estimation"

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Almradi, Ahmed M., and Sohail A. Dianat. "NDA SNR and CRLB Estimation Over MISO with STBC Channels." International Journal of Business Data Communications and Networking 8, no. 4 (October 2012): 1–16. http://dx.doi.org/10.4018/jbdcn.2012100101.

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This paper discusses the problem of Non Data Aided (NDA) Signal to Noise Ratio (SNR) estimation of Binary Phase Shift keying (BPSK) modulated signals using the Expectation Maximization (EM) Algorithm. In addition, the Cramer-Rao Lower Bounds (CRLB) for the estimation of Data Aided (DA) and Non Data Aided (NDA) Signal to Noise Ratio (SNR) estimation is derived. Multiple Input Single Output (MISO) channels with Space Time Block Codes (STBC) is used. The EM algorithm is a method that finds the Maximum Likelihood (ML) solution iteratively when there are unobserved (hidden or missing) data. Extension of the proposed approach to other types of linearly modulated signals in estimating SNR is straight forward. The performance of the estimator is assessed using the NDA CRLBs. Alamouti coding technique is used in this paper with two transmit antennas and one receive antenna. The authors’ assumption is that the received signal is corrupted by additive white Gaussian noise (AWGN) with unknown variance, and scaled by fixed unknown complex channel gain. Monte Carlo simulations are used to show that the proposed estimator offers a substantial improvement over the conventional Single Input Single Output (SISO) NDA SNR estimator due to the use of the statistical dependences in space and time. Moreover, the proposed NDA SNR estimator works close to the NDA SNR estimator over Single Input Multiple Output (SIMO) channels.
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An, Qi, Zi-shu He, Hui-yong Li, and Yong-hua Li. "Phase Clustering Based Modulation Classification Algorithm for PSK Signal over Wireless Environment." Mobile Information Systems 2016 (2016): 1–11. http://dx.doi.org/10.1155/2016/2398464.

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Promptitude and accuracy of signals’ non-data-aided (NDA) identification is one of the key technology demands in noncooperative wireless communication network, especially in information monitoring and other electronic warfare. Based on this background, this paper proposes a new signal classifier for phase shift keying (PSK) signals. The periodicity of signal’s phase is utilized as the assorted character, with which a fractional function is constituted for phase clustering. Classification and the modulation order of intercepted signals can be achieved through its Fast Fourier Transform (FFT) of the phase clustering function. Frequency offset is also considered for practical conditions. The accuracy of frequency offset estimation has a direct impact on its correction. Thus, a feasible solution is supplied. In this paper, an advanced estimator is proposed for estimating the frequency offset and balancing estimation accuracy and range under low signal-to-noise ratio (SNR) conditions. The influence on estimation range brought by the maximum correlation interval is removed through the differential operation of the autocorrelation of the normalized baseband signal raised to the power ofQ. Then, a weighted summation is adopted for an effective frequency estimation. Details of equations and relevant simulations are subsequently presented. The estimator proposed can reach an estimation accuracy of10-4even when the SNR is as low as-15 dB. Analytical formulas are expressed, and the corresponding simulations illustrate that the classifier proposed is more efficient than its counterparts even at low SNRs.
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Chudnikov, V. V., and B. I. Shakhtarin. "Adaptive Signal Frequency Estimation." Herald of the Bauman Moscow State Technical University. Series Instrument Engineering, no. 6 (129) (December 2019): 41–49. http://dx.doi.org/10.18698/0236-3933-2019-6-41-49.

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The paper introduces an adaptive algorithm for estimating the frequencies of harmonic components in the signal against the background of additive white noise. This method is iterative, which distinguishes it from the periodogram and parametric spectral estimation methods. The key feature of the algorithm is that it gives a reasonably accurate estimation only for the preset number of harmonic components included in the signal under study. In the original discrete signal, a frequency search was performed at each time sample using the gradient descent method. Frequency estimation is made when the frequency error value tends to a certain value. The search is based on the representation of the value of the current sample of the harmonic signal of a known frequency through the two previous values. Knowing the number of components included in the original signal sequence, it is possible to form the resulting sequence containing only residual noise samples. A mathematical model of the algorithm is given, its work is simulated for different conditions of application, the accuracy of the algorithm, i.e., frequency estimation, and the number of iterations for various signal-to-noise ratios are shown.
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Zhou, Qian, Si Wei Zhao, Jia Si Wei, Hao Yan, and Hui Zhao. "Parameter Estimation of Photoacoustic Signal for Glucose Solutions Using Laplace Wavelet Correlation Filtering and Least Square Estimation." Applied Mechanics and Materials 475-476 (December 2013): 225–35. http://dx.doi.org/10.4028/www.scientific.net/amm.475-476.225.

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In photoacoustic (PA) technology, piezoelectrical transducer (PZT) is the most frequently used detector for the PA signal generated by weakly absorbing liquids. Although PZT has high sensitivity, its output signals are distorted and become exponentially damped sinusoidal signal [, which is a barrier to extract correct information of glucose. Therefore it is necessary to find a proper way to extract glucose information. In this paper, a method of parameter estimation based on Laplace wavelet correlation filtering and Least Square Estimation is proposed to extract the information of PA signals, which includes signal decomposition, fitting and glucose parameter extraction. The Laplace wavelet correlation filtering is introduced to decompose piezoelectrical signals into impulse responses of single mode subsystems [2], followed by a Least Square Estimator to fit PA signal and estimate parameters of the exponentially damped sinusoidal model [3]. Good agreement between the fitting model and the PA signal is obtained. Experiments are carried out on finding meaningful parameters indicating glucose among estimated parameters of the exponentially damped sinusoidal model.
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Aggoun, Lakhdar, and Robert J. Elliott. "Celestial signal estimation." Stochastic Analysis and Applications 12, no. 4 (January 1994): 399–407. http://dx.doi.org/10.1080/07362999408809360.

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Nam, Gyeong-Mo, and Eui-Rim Jeong. "Distance Estimation Based on Deep Convolutional Neural Network Using Ultra-Wideband Signals." Journal of Computational and Theoretical Nanoscience 17, no. 7 (July 1, 2020): 3212–17. http://dx.doi.org/10.1166/jctn.2020.9163.

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Recently, high accuracy localization technique is required to provide indoor location services. The purpose of this paper is to propose a distance estimation technique based on deep convolutional neural network (DCNN) for indoor environments. Among distance estimation techniques based on wireless communication signals, the use of ultra-wideband (UWB) signals has the advantage of high accuracy in the time domain. The proposed distance estimation method uses UWB signals and proposes a new DCNN-based distance estimator. The superiority of the proposed method is confirmed through computer simulation. Widely used conventional distance estimators are based on the power threshold. The threshold is determined by signal to noise ratio (SNR) of the received signal. The arrival time of the received signal that exceeds the threshold is considered as the time-of-arrival (ToA) and the distance between transmitter and receiver is obtained from the ToA. On the other hand, the proposed distance estimator requires only the received signal without SNR estimation, which make the proposed technique simpler to implement. According to computer simulation, the conventional method is highly sensitive to SNR and distance. In contrast, the proposed method shows less than 2 m root mean square error (RMSE) performance in a wide range of SNR and the RMSE performance is not degraded in long distances. The proposed distance estimator shows excellent distance estimation performance at low SNR and long distance, so it can be applied to indoor localization system of large indoor space and can be used for precise location service.
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Xu, Wu, Yu, and Guang. "A Robust Direction of Arrival Estimation Method for Uniform Circular Array." Sensors 19, no. 20 (October 12, 2019): 4427. http://dx.doi.org/10.3390/s19204427.

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Estimating the Direction of Arrival (DOA) is a basic and crucial problem in array signal processing. The existing DOA methods fail to obtain reliable and accurate results when noise and reverberation occur in real applications. In this paper, an accurate and robust estimation method for estimating the DOA of sources signal is proposed. Incorporating the Estimating Signal Parameters via Rotational Invariance Techniques (ESPRIT) algorithm with the RANdom SAmple Consensus (RANSAC) algorithm gives rise to the RAN-ESPRIT method, which removes outliers automatically in noise-corrupted environments. In this work, a uniform circular array (UCA) is converted into a virtual uniform linear array (ULA) to begin with. Then, the covariance matrix of the received signals of the virtual linear array is reconstructed, and the ESPRIT algorithm is deployed to estimate initial DOA of the source signal. Finally, the modified RANSAC method with automatically selected thresholds is used to fit the source signal to obtain accurate DOA. The proposed method can remove the unreliable DOA feature data and leads to more accuracy of DOA estimation of source signals in reverberation environments. Experimental results demonstrate that the proposed method is more robust and efficient compared to the traditional methods (i.e., ESPRIT, TLS-ESPRIT).
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Virosztek, Tamás, and István Kollár. "Theoretical Limits of Parameter Estimation Based on Quantized Data." Periodica Polytechnica Electrical Engineering and Computer Science 61, no. 4 (August 17, 2017): 312. http://dx.doi.org/10.3311/ppee.10224.

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Parameter estimation of band-limited periodic signals (sine and multisine waves) is a very common task in the field of measurement technology and control engineering. In the overwhelming majority of data acquisition and control systems the analog signals of the real world are sampled an quantized using analog-to-digital converters (ADCs). To estimate the parameters of the analog signal and the parameters of the quantizer from the same measurement record is an obvious need in these cases. The parameters of the recorded signal can be used to calculate the response of our system (e.g. signals of the actuators) while the parameters of the quantizer can be used to identify the transfer characteristic of the measurement channel. Maximum likelihood (ML) estimation of the quantizer and analog signal parameters has been developed to perform this task and to provide asymptotically unbiased and efficient estimators for the quantizer and signal parameters. This paper investigates the theoretical limits of this kind of estimation: provides the Cramér-Rao Lower Bound (CRLB) for the covariance of the achieved estimators and compares them to CRLB values obtained using less complex signal and channel models. This article also provides a comparison of the empirical covariance of estimator populations achieved different ways to the CRLB of estimation. The major tendencies are drawn and explanation for them is provided as well.
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Mohammed, Bassim Sayed, and Dalya Khalid Hasan. "Estimating Angle of Arrival (AOA) for Wideband Signal by Sensor Delay Line (SDL) and Tapped Delay Line (TDL) Processors." Journal of Engineering 24, no. 4 (March 31, 2018): 96. http://dx.doi.org/10.31026/j.eng.2018.04.07.

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Angle of arrival (AOA) estimation for wideband signal becomes more necessary for modern communication systems like Global System for Mobile (GSM), satellite, military applications and spread spectrum (frequency hopping and direct sequence). Most of the researchers are focusing on how to cancel the effects of signal bandwidth on AOA estimation performance by using a transversal filter (tap delay line) (TDL). Most of the researchers were using two elements array antenna to study these effects. In this research, a general case of proposed (M) array elements is used. A transversal filter (TDL) in phase adaptive array antenna system is used to calculate the optimum number of taps required to compensate these effect. The proposed system uses a phase adaptive array antenna in conjunction with LMS algorithm to work an angle of arrival (AOA) estimator for wideband signals rather than interference canceller. An alternative solution to compensate for the effect of signal bandwidth is proposed by using sensor delay line (SDL) instead of fixed delay unit since it has variable time sampling in the time domain and not fixed time delay, depending on the angle of arrival of received signals. The proposed system has the ability to estimate two parameters for received signals simultaneously (the output Signal to Noise Ratio (SNR) and AOA), unlike others systems which estimate AOA only. The comparison of the simulation results with Multiple Signal Classification (MUSIC) technique showed that the proposed system gives good results for estimating AOA and the output SNR for wideband signals. (SDL) processor shows better performance result than (TDL) processor. MUSIC technique with both (SDL) and (TDL) processors shows unacceptable results for estimating (AOA) for the wideband signal.
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Kumar, R. Suresh, and P. Manimegalai. "Detection and Separation of Eeg Artifacts Using Wavelet Transform." International Journal of Informatics and Communication Technology (IJ-ICT) 7, no. 3 (December 1, 2018): 149. http://dx.doi.org/10.11591/ijict.v7i3.pp149-156.

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Bio-medical signal processing is one of the most important techniques of multichannel sensor network and it has a substantial concentration in medical application. However, the real-time and recorded signals in multisensory instruments contains different and huge amount of noise, and great work has been completed in developing most favorable structures for estimating the signal source from the noisy signal in multichannel observations. Methods have been developed to obtain the optimal linear estimation of the output signal through the Wide-Sense-Stationary (WSS) process with the help of time-invariant filters. In this process, the input signal and the noise signal are assumed to achieve the linear output signal. During the process, the non-stationary signals arise in the bio-medical signal processing in addition to it there is no effective structure to deal with them. Wavelets transform has been proved to be the efficient tool for handling the non-stationary signals, but wavelet provide any possible way to approach multichannel signal processing. Based on the basic structure of linear estimation of non-stationary multichannel data and statistical models of spatial signal coherence acquire through the wavelet transform in multichannel estimation. The above methods can be used for Electroencephalography (EEG) signal denoising through the original signal and then implement the noise reduction technique to evaluate their performance such as SNR, MSE and computation time.
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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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Books on the topic "Signal estimation"

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Signal detection and estimation. 2nd ed. Boston: Artech House, 2005.

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Barkat, Mourad. Signal detection and estimation. Boston: Artech House, 1991.

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Swagata, Nandi, and SpringerLink (Online service), eds. Statistical Signal Processing: Frequency Estimation. India: Springer India, 2012.

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Helstrom, Carl W. Elements of signal detection and estimation. Englewood Cliffs, N.J: PTR Prentice Hall, 1995.

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Fante, Ronald L. Signal analysis and estimation: An introduction. New York: Wiley, 1988.

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Detection, estimation, and modulation theory. New York: Wiley, 2001.

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Poor, H. Vincent. An introduction to signal detection and estimation. New York: Springer-Verlag, 1988.

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An introduction to signal detection and estimation. 2nd ed. New York: Springer-Verlag, 1994.

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Kay, Steven M. Fundamentals of statistical signal processing: Estimation theory. Englewood Cliffs, NJ: Prentice-Hall International, 1993.

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Levy, Bernard C. Principles of Signal Detection and Parameter Estimation. Boston, MA: Springer Science+Business Media, LLC, 2008.

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Book chapters on the topic "Signal estimation"

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Chonavel, Thierry. "Adaptive Estimation." In Statistical Signal Processing, 231–48. London: Springer London, 2002. http://dx.doi.org/10.1007/978-1-4471-0139-0_16.

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Hlawatsch, Franz. "Signal Estimation and Signal Detection." In The Kluwer International Series in Engineering and Computer Science, 125–52. Boston, MA: Springer US, 1998. http://dx.doi.org/10.1007/978-1-4757-2815-6_6.

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Chonavel, Thierry. "Parametric Spectral Estimation." In Statistical Signal Processing, 159–84. London: Springer London, 2002. http://dx.doi.org/10.1007/978-1-4471-0139-0_13.

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Apte, Shaila Dinkar. "Detection and Estimation." In Random Signal Processing, 127–52. Boca Raton : CRC Press, 2018.: CRC Press, 2017. http://dx.doi.org/10.1201/9781315155357-4.

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Nandi, Swagata, and Debasis Kundu. "Estimation of Frequencies." In Statistical Signal Processing, 39–65. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-6280-8_3.

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Ricker, Dennis W. "Detection and Estimation." In Echo Signal Processing, 69–152. Boston, MA: Springer US, 2003. http://dx.doi.org/10.1007/978-1-4615-0312-5_3.

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Kundu, Debasis, and Swagata Nandi. "Estimation of Frequencies." In Statistical Signal Processing, 17–43. India: Springer India, 2012. http://dx.doi.org/10.1007/978-81-322-0628-6_3.

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Mohanty, Nirode. "Random Signals, Estimation, and Filtering." In Signal Processing, 278–456. Dordrecht: Springer Netherlands, 1987. http://dx.doi.org/10.1007/978-94-011-7044-4_3.

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Veloni, Anastasia, Nikolaos I. Miridakis, and Erysso Boukouvala. "Linear Estimation." In Digital and Statistical Signal Processing, 455–78. Boca Raton : Taylor & Francis, a CRC title, part of the Taylor & Francis imprint, a member of the Taylor & Francis Group, the academic division of T&F Informa, plc, 2018.: CRC Press, 2018. http://dx.doi.org/10.1201/9780429507526-9.

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Chonavel, Thierry. "Non-parametric Spectral Estimation." In Statistical Signal Processing, 139–58. London: Springer London, 2002. http://dx.doi.org/10.1007/978-1-4471-0139-0_12.

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Conference papers on the topic "Signal estimation"

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Schell, S. V., and W. A. Gardner. "Progress on signal-selective direction finding." In Fifth ASSP Workshop on Spectrum Estimation and Modeling. IEEE, 1990. http://dx.doi.org/10.1109/spect.1990.205563.

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Cadzow, J. A. "Signal subspace method of multiple source location." In Fifth ASSP Workshop on Spectrum Estimation and Modeling. IEEE, 1990. http://dx.doi.org/10.1109/spect.1990.205549.

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Valenzuela, H. M., and N. K. Bose. "Bilinear time series in non-Gaussian signal modeling." In Fifth ASSP Workshop on Spectrum Estimation and Modeling. IEEE, 1990. http://dx.doi.org/10.1109/spect.1990.205536.

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McDonnell, Mark D. "Signal Estimation Via Averaging of Coarsely Quantised Signals." In 2007 Information, Decision and Control. IEEE, 2007. http://dx.doi.org/10.1109/idc.2007.374533.

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Tufts, D. W. "The effects of perturbations on matrix-based signal processing." In Fifth ASSP Workshop on Spectrum Estimation and Modeling. IEEE, 1990. http://dx.doi.org/10.1109/spect.1990.205566.

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Paldan, Jesse R., Jeremy P. Gray, and Vladimir V. Vantsevich. "Sensor Signal Limitations in Wheel Rotational Kinematics Estimation Model." In ASME 2015 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/dscc2015-9769.

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Abstract:
Wheel encoders play an important role in providing information about rotational kinematics of vehicle wheels. The sensor signals are utilized in critical vehicle systems responsible for vehicle safety, traction and braking performance, and stability of motion. This paper starts with an analysis of different types of sensors that have been used in rotational wheel kinematics estimations and controls. The main attention is given to sensor signal limitations related to the accuracy of measurement and response time that are important for agile-to-real-time tire dynamics estimation. A detailed analysis of the wheel rotational velocity estimation process is presented for a conventional Hall Effect digital sensor. Through an analytical modelling, it is shown that this sensor can limit its accuracy due to an increased time for signal information assembly caused by the number of impulses and transient (unsteady) rotational motion in unstable road conditions. A new concept of a rotational kinematics sensor is proposed and modeled as a multi-domain mechatronic system that includes new mechanical elements as well as electrical and magnetic components. The sensor concept provides a smooth continuous signal through the full rotational angle of the wheel and precise information about the rotational velocity and its changes in different unstable road conditions. Computational examples of both sensors (digital and proposed) are demonstrated with the use of a quarter-car model moving over a random road profile in stochastic gripping and rolling resistance conditions. A comparison of the two sensors’ accuracy to estimate the rotational velocity of the wheel is done with regard to an “ideal” sensor with a unity transfer function.
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Guanghan Xu and T. Kailath. "A new array signal processing method via exploitation of cyclostationarity." In Fifth ASSP Workshop on Spectrum Estimation and Modeling. IEEE, 1990. http://dx.doi.org/10.1109/spect.1990.205553.

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P Nascimento, Jose M., and Jose M. Bioucas-Dias. "Hyperspectral signal subspace estimation." In 2007 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2007. http://dx.doi.org/10.1109/igarss.2007.4423531.

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Makhoul, J., and A. Steinhardt. "The peak of a causal signal with a given average delay." In Fifth ASSP Workshop on Spectrum Estimation and Modeling. IEEE, 1990. http://dx.doi.org/10.1109/spect.1990.205584.

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Amin, M. G. "A signal subspace approach for interference locations in adaptive antenna arrays." In Fifth ASSP Workshop on Spectrum Estimation and Modeling. IEEE, 1990. http://dx.doi.org/10.1109/spect.1990.205598.

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Reports on the topic "Signal estimation"

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Varshney, Pramod K., Donald D. Welner, and Tzeta Tsao. Radar Signal Detection and Estimation Using Time-Frequency Distributions. Fort Belvoir, VA: Defense Technical Information Center, October 1995. http://dx.doi.org/10.21236/ada304818.

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Shumway, Robert H., and Sung-Eun Kim. Signal Detection and Estimation of Directional Parameters for Multiple Arrays. Fort Belvoir, VA: Defense Technical Information Center, August 2001. http://dx.doi.org/10.21236/ada400949.

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Michalopoulou, Zoi-Heleni. Ocean Acoustics and Signal Processing for Robust Detection and Estimation. Fort Belvoir, VA: Defense Technical Information Center, September 2009. http://dx.doi.org/10.21236/ada531392.

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Michalopoulou, Zoi-Heleni. Ocean Acoustics and Signal Processing for Robust Detection and Estimation. Fort Belvoir, VA: Defense Technical Information Center, September 2008. http://dx.doi.org/10.21236/ada533119.

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Candy, J. V., B. R. Illingworth, K. W. Craft, and J. E. Case. Real-time Signal Processing for Sounding Rocket Modal Frequency Estimation. Office of Scientific and Technical Information (OSTI), March 2019. http://dx.doi.org/10.2172/1548320.

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Michalopoulou, Zoi-Heleni. Ocean Acoustics and Signal Processing for Robust Detection and Estimation. Fort Belvoir, VA: Defense Technical Information Center, September 2007. http://dx.doi.org/10.21236/ada573056.

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Michalopoulou, Zoi-Heleni. Ocean Acoustics and Signal Processing for Robust Detection and Estimation. Fort Belvoir, VA: Defense Technical Information Center, September 1999. http://dx.doi.org/10.21236/ada630369.

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Michalopoulou, Zoi-Heleni. Ocean Acoustics and Signal Processing for Robust Detection and Estimation. Fort Belvoir, VA: Defense Technical Information Center, September 2003. http://dx.doi.org/10.21236/ada629910.

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Gardner, William A. Exploitation of Cyclostationarity for Signal-Parameter Estimation and System Identification. Fort Belvoir, VA: Defense Technical Information Center, June 1993. http://dx.doi.org/10.21236/ada267137.

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Halverson, Don. Nonlocal Methods for Signal Detection and Estimation in the Dependent Nonstationary Environment. Fort Belvoir, VA: Defense Technical Information Center, November 1993. http://dx.doi.org/10.21236/ada278472.

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