Relevant bibliographies by topics / Estimation of the process noise

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Academic literature on the topic 'Estimation of the process noise'

Author: Grafiati
Published: 28 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Estimation of the process noise"

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Gillard, Nicolas, Étienne Belin, and François Chapeau-Blondeau. "Stochastic Resonance with Unital Quantum Noise." Fluctuation and Noise Letters 18, no. 03 (July 16, 2019): 1950015. http://dx.doi.org/10.1142/s0219477519500159.

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The fundamental quantum information processing task of estimating the phase of a qubit is considered. Following quantum measurement, the estimation efficiency is evaluated by the classical Fisher information which determines the best performance limiting any estimator and achievable by the maximum likelihood estimator. The estimation process is analyzed in the presence of decoherence represented by essential quantum noises that can affect the qubit and belonging to the broad class of unital quantum noises. Such a class especially contains the bit-flip, the phase-flip, the depolarizing noises, or the whole family of Pauli noises. As the level of noise is increased, we report the possibility of non-standard behaviors where the estimation efficiency does not necessarily deteriorate uniformly, but can experience non-monotonic variations. Regimes are found where higher noise levels prove more favorable to estimation. Such behaviors are related to stochastic resonance effects in signal estimation, shown here feasible for the first time with unital quantum noises. The results provide enhanced appreciation of quantum noise or decoherence, manifesting that it is not always detrimental for quantum information processing.
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Wang, Chen, Yao-Wu Shi, Lan-Xiang Zhu, Li-Fei Deng, Yi-Ran Shi, and De-Min Wang. "Auto-regressive moving average parameter estimation for 1/f process under colored Gaussian noise background." Journal of Algorithms & Computational Technology 13 (January 2019): 174830261986743. http://dx.doi.org/10.1177/1748302619867439.

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Current algorithms for estimating auto-regressive moving average parameters of transistor 1/f process are usually under noiseless background. Transistor noises are measured by a non-destructive cross-spectrum measurement technique, with transistor noise first passing through dual-channel ultra-low noise amplifiers, then inputting the weak signals into data acquisition card. The data acquisition card collects the voltage signals and outputs the amplified noise for further analysis. According to our studies, the output transistor 1/f noise can be characterized more accurately as non-Gaussian α-stable distribution rather than Gaussian distribution. We define and consistently estimate the samples normalized cross-correlations of linear SαS processes, and propose a samples normalized cross-correlations-based auto-regressive moving average parameter estimation method effective in noisy environments. Simulation results of auto-regressive moving average parameter estimation exhibit good performance.
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Shin, Vladimir, Rebbecca T. Y. Thien, and Yoonsoo Kim. "Receding Horizon Least Squares Estimator with Application to Estimation of Process and Measurement Noise Covariances." Mathematical Problems in Engineering 2018 (November 19, 2018): 1–15. http://dx.doi.org/10.1155/2018/5303694.

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This paper presents a noise covariance estimation method for dynamical models with rectangular noise gain matrices. A novel receding horizon least squares criterion to achieve high estimation accuracy and stability under environmental uncertainties and experimental errors is proposed. The solution to the optimization problem for the proposed criterion gives equations for a novel covariance estimator. The estimator uses a set of recent information with appropriately chosen horizon conditions. Of special interest is a constant rectangular noise gain matrices for which the key theoretical results are obtained. They include derivation of a recursive form for the receding horizon covariance estimator and iteration procedure for selection of the best horizon length. Efficiency of the covariance estimator is demonstrated through its implementation and performance on dynamical systems with an arbitrary number of process and measurement noises.
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Moon, Todd K., and Jacob H. Gunther. "Estimation of Autoregressive Parameters from Noisy Observations Using Iterated Covariance Updates." Entropy 22, no. 5 (May 19, 2020): 572. http://dx.doi.org/10.3390/e22050572.

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Estimating the parameters of the autoregressive (AR) random process is a problem that has been well-studied. In many applications, only noisy measurements of AR process are available. The effect of the additive noise is that the system can be modeled as an AR model with colored noise, even when the measurement noise is white, where the correlation matrix depends on the AR parameters. Because of the correlation, it is expedient to compute using multiple stacked observations. Performing a weighted least-squares estimation of the AR parameters using an inverse covariance weighting can provide significantly better parameter estimates, with improvement increasing with the stack depth. The estimation algorithm is essentially a vector RLS adaptive filter, with time-varying covariance matrix. Different ways of estimating the unknown covariance are presented, as well as a method to estimate the variances of the AR and observation noise. The notation is extended to vector autoregressive (VAR) processes. Simulation results demonstrate performance improvements in coefficient error and in spectrum estimation.
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Tan, Hanlin, Huaxin Xiao, Shiming Lai, Yu Liu, and Maojun Zhang. "Pixelwise Estimation of Signal-Dependent Image Noise Using Deep Residual Learning." Computational Intelligence and Neuroscience 2019 (September 9, 2019): 1–12. http://dx.doi.org/10.1155/2019/4970508.

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In traditional image denoising, noise level is an important scalar parameter which decides how much the input noisy image should be smoothed. Existing noise estimation methods often assume that the noise level is constant at every pixel. However, real-world noise is signal dependent, or the noise level is not constant over the whole image. In this paper, we attempt to estimate the precise and pixelwise noise level instead of a simple global scalar. To the best of our knowledge, this is the first work on the problem. Particularly, we propose a deep convolutional neural network named “deep residual noise estimator” (DRNE) for pixelwise noise-level estimation. We carefully design the architecture of the DRNE, which consists of a stack of customized residual blocks without any pooling or interpolation operation. The proposed DRNE formulates the process of noise estimation as pixel-to-pixel prediction. The experimental results show that the DRNE can achieve better performance on nonhomogeneous noise estimation than state-of-the-art methods. In addition, the DRNE can bring denoising performance gains in removing signal-dependent Gaussian noise when working with recent deep learning denoising methods.
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Bianchi, Federico, Simone Formentin, and Luigi Piroddi. "Process noise covariance estimation via stochastic approximation." International Journal of Adaptive Control and Signal Processing 34, no. 1 (November 7, 2019): 63–76. http://dx.doi.org/10.1002/acs.3068.

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Ali, Suhad A., C. Elaf A. Abbood, and Shaymaa Abdu LKadhm. "Salt and Pepper Noise Removal Using Resizable Window and Gaussian Estimation Function." International Journal of Electrical and Computer Engineering (IJECE) 6, no. 5 (October 1, 2016): 2219. http://dx.doi.org/10.11591/ijece.v6i5.11641.

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<p class="Default">Most types of the images are corrupted in many ways that because exposed to different types of noises. The corruptions happen during transmission from space to another, during storing or capturing. Image processing has various techniques to process the image. Before process the image, there is need to remove noise that corrupt the image and enhance it to be as near as to the original image. This paper proposed a new method to process a particular common type of noise. This method removes salt and pepper noise by using many techniques. First, detect the noisy pixel, then increasing the size of the pixel window depending on some criteria to be enough to estimate the results. To estimates the pixels of image, the Gaussian estimation function is used. The resulted image quality is measured by the statistical quantity measures that's Peak Signal-to-Noise Ratio (PSNR) and The Structural Similarity (SSIM) metrics. The results illustrate the quality of the enhanced image compared with the other traditional techniques. The slight gradual of SSIM metric described the performance of the proposed method with high increasing of noise levels.</p>
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Ali, Suhad A., C. Elaf A. Abbood, and Shaymaa Abdu LKadhm. "Salt and Pepper Noise Removal Using Resizable Window and Gaussian Estimation Function." International Journal of Electrical and Computer Engineering (IJECE) 6, no. 5 (October 1, 2016): 2219. http://dx.doi.org/10.11591/ijece.v6i5.pp2219-2224.

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<p class="Default">Most types of the images are corrupted in many ways that because exposed to different types of noises. The corruptions happen during transmission from space to another, during storing or capturing. Image processing has various techniques to process the image. Before process the image, there is need to remove noise that corrupt the image and enhance it to be as near as to the original image. This paper proposed a new method to process a particular common type of noise. This method removes salt and pepper noise by using many techniques. First, detect the noisy pixel, then increasing the size of the pixel window depending on some criteria to be enough to estimate the results. To estimates the pixels of image, the Gaussian estimation function is used. The resulted image quality is measured by the statistical quantity measures that's Peak Signal-to-Noise Ratio (PSNR) and The Structural Similarity (SSIM) metrics. The results illustrate the quality of the enhanced image compared with the other traditional techniques. The slight gradual of SSIM metric described the performance of the proposed method with high increasing of noise levels.</p>
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Feng, Bo, Mengyin Fu, Hongbin Ma, Yuanqing Xia, and Bo Wang. "Kalman Filter With Recursive Covariance Estimation—Sequentially Estimating Process Noise Covariance." IEEE Transactions on Industrial Electronics 61, no. 11 (November 2014): 6253–63. http://dx.doi.org/10.1109/tie.2014.2301756.

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Jwo, Dah-Jing, and Chun-Fan Pai. "Incorporation of Neural Network State Estimator for GPS Attitude Determination." Journal of Navigation 57, no. 1 (January 2004): 117–34. http://dx.doi.org/10.1017/s0373463303002625.

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The Global Positioning System (GPS) can be employed as a free attitude determination interferometer when carrier phase measurements are utilized. Conventional approaches for the baseline vectors are essentially based on the least-squares or Kalman filtering methods. The raw attitude solutions are inherently noisy if the solutions of baseline vectors are obtained based on the least-squares method. The Kalman filter attempts to minimize the error variance of the estimation errors and will provide the optimal result while it is required that the complete a priori knowledge of both the process noise and measurement noise covariance matrices are available. In this article, a neural network state estimator, which replaces the Kalman filter, will be incorporated into the attitude determination mechanism for estimating the attitude angles from the noisy raw attitude solutions. Employing the neural network estimator improves robustness compared to the Kalman filtering method when uncertainty in noise statistical knowledge exists. Simulation is conducted and a comparative evaluation based on the neural network estimator and Kalman filter is provided.
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Dissertations / Theses on the topic "Estimation of the process noise"

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Anilkumar, A. K. "Application Of Controlled Random Search Optimization Technique In MMLE With Process Noise." Thesis, Indian Institute of Science, 2000. http://hdl.handle.net/2005/232.

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Generally in most of the applications of estimation theory using the Method of Maximum Likelihood Estimation (MMLE) to dynamical systems one deals with a situation where only the measurement noise alone is present. However in many present day applications where modeling errors and random state noise input conditions occur it has become necessary for MMLE to handle measurement noise as well as process noise. The numerical algorithms accounting for both measurement and process noise require significantly an order of magnitude higher computer time and memory. Further more, implementation difficulties and convergence problems are often encountered. Here one has to estimate the quantities namely, the initial state error covariance matrix Po, measurement noise covariance matrix R, the process noise covariance matrix Q and the system parameter 0 and the present work deals with the above. Since the above problem is fairly involved we need to have a good reference solution. For this purpose we utilize the approach and results of Gemson who considered the above problem via the extended Kalman filter (EKF) route to compare the present results from the MMLE route. The EKF uses the unknown parameters as additional states unlike in MMLE which uses only the system states. Chapter 1 provides a brief historical perspective followed by parameter identification in the presence of process and measurement noises. The earlier formulations such as natural, innovation, combined, and adaptive approaches are discussed. Chapter 2 deals with the heuristic adaptive tuning of the Kalman filter parameters for the matrices Q and R by Myers and Tapley originally developed for state estimation problems involving satellite orbit estimation. It turns out that for parameter estimation problems apart from the above matrices even the choice of the initial covariance matrix Po is crucial for obtaining proper parameter estimates with a finite amount of data and for this purpose the inverse of the information matrix for Po is used. This is followed by a description of the original Controlled Random Search (CRS) of Price and its variant as implemented and used in the present work to estimate or tune Q, R, and 0 which is the aim of the present work. The above help the reader to appreciate the setting under which the present study has been carried out. Chapter 3 presents the results and the analysis of the estimation procedure adopted with respect to a specific case study of the lateral dynamics of an aircraft involving 15 unknown parameters. The reference results for the present work are the ones based on the approach of Gemson and Ananthasayanam (1998). The present work proceeds in two phases. In the first case (i) the EKF estimates for Po, Q, and R are used to obtain 0 and in the second case (ii) the estimate of Po and Q together with a reasonable choice of R are utilized to obtain 0 from the CRS algorithm. Thus one is able to assess the capability of the CRS to estimate only the unknown parameters. The next Chapter 4 presents the results of utilizing the CRS algorithm with R based on a reasonable choice and for Po from the inverse of the information matrix to estimate both Q and 0. This brings out the efficiency of MMLE with CRS algorithm in the estimation of unknown process noise characteristics and unknown parameters. Thus it demonstratesthofcdifficult Q can be estimated using CRS technique without the attendant difficulties of the earlier MMLE formulations in dealing with process noise. Chapter 5 discusses the - implementation of CRS to estimate the unknown measurement noise covariance matrix R together with the unknown 0 by utilizing the values of Po and Q obtained through EKF route. The effect of variation of R in the parameter estimation procedure is also highlighted in This Chapter. This Chapter explores the importance of Po in the estimation procedure. It establishes the importance of Po though most of the earlier works do not appear to have recognized such a feature. It turned out that the CRS algorithm does not converge when some arbitrary value of Po is chosen. It has to be necessarily obtained from a scouting pass of the EKF. Some sensitivity studies based on variations of Po shows its importance. Further studies shows the sequence of updates, the random nature of process and measurement noise effects, the deterministic nature of the parameter, play a critical role in the convergence of the algorithm. The last Chapter 6 presents the conclusions from the present work and suggestions for further work.
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Barajas, Leandro G. "Process Control in High-Noise Environments Using A Limited Number Of Measurements." Diss., Georgia Institute of Technology, 2003. http://hdl.handle.net/1853/7741.

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The topic of this dissertation is the derivation, development, and evaluation of novel hybrid algorithms for process control that use a limited number of measurements and that are suitable to operate in the presence of large amounts of process noise. As an initial step, affine and neural network statistical process models are developed in order to simulate the steady-state system behavior. Such models are vitally important in the evaluation, testing, and improvement of all other process controllers referred to in this work. Afterwards, fuzzy logic controller rules are assimilated into a mathematical characterization of a model that includes the modes and mode transition rules that define a hybrid hierarchical process control. The main processing entity in such framework is a closed-loop control algorithm that performs global and then local optimizations in order to asymptotically reach minimum bias error; this is done while requiring a minimum number of iterations in order to promptly reach a desired operational window. The results of this research are applied to surface mount technology manufacturing-lines yield optimization. This work achieves a practical degree of control over the solder-paste volume deposition in the Stencil Printing Process (SPP). Results show that it is possible to change the operating point of the process by modifying certain machine parameters and even compensate for the difference in height due to change in print direction.
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Constant, Camille. "Modélisation stochastique et analyse statistique de la pulsatilité en neuroendocrinologie." Thesis, Poitiers, 2019. http://www.theses.fr/2019POIT2330.

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L’objectif de cette thèse est de proposer plusieurs modèles probabilistes pour représenter l’activité calcique des neurones et comprendre son implication dans la sécrétion d’hormone GnRH. Ce travail s’appuie sur des expériences réalisées à l’INRA Centre Val-de-Loire. Le Chapitre 1 propose une modélisation continue, où nous étudions un processus markovien de type shot-noise. Le Chapitre 2 étudie un modèle discret de type AR(1) basé sur la discrétisation du modèle du Chapitre 1 et propose une première estimation des paramètres. Le Chapitre 3 propose un autre modèle discret de type AR(1) où les innovations sont la somme d’une variable de Bernouilli et d’une variable gaussienne représentant un bruit, avec prise en compte d’une tendance linéaire. Des estimations des paramètres sont proposées dans le but d’une détection des sauts dans les trajectoires des neurones. Le Chapitre 4 étudie une expérience biologique comportant 33 neurones. Avec la modélisation du Chapitre 3, nous détectons des instants de synchronisation (saut simultané d’une grande proportion des neurones de l’expérience) puis à l’aide de simulations, nous testons la qualité de la méthode utilisée et la comparons à une méthode expérimentale
The aim of this thesis is to propose several models representing neuronal calcic activity and unsderstand its applicatition in the secretion of GnRH hormone. This work relies on experience realised in INRA Centre Val de Loire. Chapter 1 proposes a continuous model, in which we examine a Markov process of shot-noise type. Chapter 2 studies a discrete model type AR(1), based on a discretization of the model from Chapter 1 and proposes a first estimation of the parameters. Chapter 3 proposes another dicrete model, type AR(1), in which the innovations are the sum of a Bernouilli variable and a Gaussian variable representing a noise, and taking into account a linear drift . Estimations of the parameters are given in order to detect spikes in neuronal paths. Chapter 4 studies a biological experience involving 33 neurons. With the modelisation of Chapter 3, we detect synchronization instants (simultaneous spkike of a high proportion of neurons of the experience) and then, using simulations, we test the quality of the method that we used and we compare it to an experimental approach
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Sun, Yucheng. "Essays in volatility estimation based on high frequency data." Doctoral thesis, Universitat Pompeu Fabra, 2017. http://hdl.handle.net/10803/402831.

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Based on high-frequency price data, this thesis focuses on estimating the realized covariance and the integrated volatility of asset prices, and applying volatility estimation to price jump detection. The first chapter uses the LASSO procedure to regularize some estimators of high dimensional realized covariance matrices. We establish theoretical properties of the regularized estimators that show its estimation precision and the probability that they correctly reveal the network structure of the assets. The second chapter proposes a novel estimator of the integrated volatility which is the quadratic variation of the continuous part in the price process. This estimator is obtained by truncating the two-scales realized variance estimator. We show its consistency in the presence of market microstructure noise and finite or infinite activity jumps in the price process. The third chapter employs this estimator to design a test to explore the existence of price jumps with noisy price data.
Basándonos en datos de precios de alta frecuencia, esta tesis se centra en la estimación de la covarianza realizada y la volatilidad integrada de precios de activos, y la aplicación de la estimación de la volatilidad para la detección de saltos en los precios. El primer capítulo utiliza el procedimiento LASSO para regularizar algunos estimadores de matrices de covarianza realizada de alta dimensión. Establecemos propiedades teóricas de los estimadores regularizados que muestran su precisión de estimación y la probabilidad de que revelen correctamente la estructura de red de los activos. En el segundo capítulo se propone un nuevo estimador de la volatilidad integrada que es la variación cuadrática de la parte continua en el proceso de precios. Este estimador se obtiene truncando el estimador de varianza realizado en dos escalas. Demostramos su consistencia en presencia de ruido de microestructura del mercado y saltos de actividad finitos o infinitos en el proceso de precios. El tercer capítulo emplea este estimador para diseñar un test para explorar la existencia de saltos en los precios con ruido.
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Jaoua, Nouha. "Estimation Bayésienne non Paramétrique de Systèmes Dynamiques en Présence de Bruits Alpha-Stables." Phd thesis, Ecole Centrale de Lille, 2013. http://tel.archives-ouvertes.fr/tel-00929691.

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Dans un nombre croissant d'applications, les perturbations rencontrées s'éloignent fortement des modèles classiques qui les modélisent par une gaussienne ou un mélange de gaussiennes. C'est en particulier le cas des bruits impulsifs que nous rencontrons dans plusieurs domaines, notamment celui des télécommunications. Dans ce cas, une modélisation mieux adaptée peut reposer sur les distributions alpha-stables. C'est dans ce cadre que s'inscrit le travail de cette thèse dont l'objectif est de concevoir de nouvelles méthodes robustes pour l'estimation conjointe état-bruit dans des environnements impulsifs. L'inférence est réalisée dans un cadre bayésien en utilisant les méthodes de Monte Carlo séquentielles. Dans un premier temps, cette problématique a été abordée dans le contexte des systèmes de transmission OFDM en supposant que les distorsions du canal sont modélisées par des distributions alpha-stables symétriques. Un algorithme de Monte Carlo séquentiel a été proposé pour l'estimation conjointe des symboles OFDM émis et des paramètres du bruit $\alpha$-stable. Ensuite, cette problématique a été abordée dans un cadre applicatif plus large, celui des systèmes non linéaires. Une approche bayésienne non paramétrique fondée sur la modélisation du bruit alpha-stable par des mélanges de processus de Dirichlet a été proposée. Des filtres particulaires basés sur des densités d'importance efficaces sont développés pour l'estimation conjointe du signal et des densités de probabilité des bruits
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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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Bareš, Robert. "Environmentally adaptive noise estimation for active sonar." Thesis, Cardiff University, 2012. http://orca.cf.ac.uk/18588/.

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Noise is frequently encountered when processing data from the natural environment, and is of particular concern for remote-sensing applications where the accuracy of data gathered is limited by the noise present. Rather than merely accepting that sonar noise results in unavoidable error in active sonar systems, this research explores various methodologies to reduce the detrimental effect of noise. Our approach is to analyse the statistics of sonar noise in trial data, collected by a long-range active sonar system in a shallow water environment, and apply this knowledge to target detection. Our detectors are evaluated against imulated targets in simulated noise, simulated targets embedded in noise-only trial data, and trial data containing real targets. First, we demonstrate that the Weibull and K-distributions offer good models of sonar noise in a cluttered environment, and that the K-distribution achieves the greatest accuracy in the tail of the distribution. We demonstrate the limitations of the Kolmogorov-Smirnov goodness-of-fit test in the context of detection by thresholding, and investigate the upper-tail Anderson-Darling test for goodness-of-fit analysis. The upper-tail Anderson-Darling test is shown to be more suitable in the context of detection by thresholding, as it is sensitive to the far-right tail of the distribution, which is of particular interest for detection at low false alarm rates. We have also produced tables of critical values for K-distributed data evaluated by the upper-tail Anderson-Darling test. Having established suitable models for sonar noise, we develop a number of detection statistics. These are based on the box-car detector, and the generalized likelihood ratio test with a Rician target model. Our performance analysis shows that both types of detector benefit from the use of the noise model provided by the K-distribution. We also demonstrate that for weak signals, our GLRT detectors are able to achieve greater probability of detection than the box-car detectors. The GLRT detectors are also easily extended to use more than one sample in a single test, an approach that we show to increase probability of detection when processing simulated targets. A fundamental difficulty in estimating model parameters is the small sample size. Many of the pings in our trial data overlap, covering the same region of the sea. It is therefore possible to make use of samples from multiple pings of a region, increasing the sample size. For static targets, the GLRT detector is easily extended to multi-ping processing, but this is not as easy for moving targets. We derive a new method of combining noise estimates over multiple pings. This calculation can be applied to either static or moving targets, and is also shown to be useful for generating clutter maps. We then perform a brief performance analysis on trial data containing real targets, where we show that in order to perform well, the GLRT detector requires a more accurate model of the target than the Rician distribution is able to provide. Despite this, we show that both GLRT and box-car detectors, when using the K-distribution as a noise model, can achieve a small improvement in the probability of detection by combining estimates of the noise parameters over multiple pings.
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Lowe, Alexander M. "Estimation of electrochemical noise impedance and corrosion rates from electrochemical noise measurements." Curtin University of Technology, School of Electrical and Computer Engineering, 2002. http://espace.library.curtin.edu.au:80/R/?func=dbin-jump-full&object_id=12723.

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Electrochemical noise refers to the spontaneous fluctuations in potential and current that can be observed on a corroding metal. The use of electrochemical noise for obtaining information on the corrosion process generates much interest in research fields. One important application is the measurement of corrosion rate. This can be achieved using the electrochemical noise of a pair of electrically coupled corroding metals to obtain an estimate of electrochemical impedance - an abstract quantity that reflects various aspects of the corrosion process.There are a number of problems associated with estimation of impedance information from the electrochemical noise data, particularly regarding data pre-treatment, accuracy and precision. In addition, the present methods are incomplete: current literature does not offer information regarding the phase of the impedance; and assumptions regarding symmetry of an electrode pair cannot be tested without additional measurements.The thesis addresses the above mentioned problems. Specifically,analysis of the impedance estimation process is given to determine how precision can be affected by various factors;a novel signal processing technique is described that is shown to yield a local optimum precision;the application of the proposed signal processing to time varying systems is demonstrated by use of a time varying, frequency dependent impedance estimate;a technique for recovering phase information, given certain conditions, is suggested so that Nyquist impedance diagrams can be constructed; anda technique for testing the symmetry of a coupled pair of corroding metals is described.An integral part of electrochemical noise analysis is the software used for numerical computation. The Matlab package from MathWorks inc. provides an extensible platform for electrochemical noise analysis. Matlab code is provided in Appendix A to implement ++
much of the theory discussed in the thesis.Impedance analysis and many other electrochemical corrosion monitoring techniques are primarily used for uniform corrosion, where the corrosion patterns occur uniformly over the exposed surface. In order to map localised corrosion, where the corrosion is typically concentrated within a small area, a wire beam electrode can be used. A wire beam electrode is a surface that is divided into a matrix of mini-electrodes so that the corrosion rate at different points can be monitored. However, manual connection of each mini-electrode to the measurement device can prove cumbersome. The final chapter of this thesis describes the design and testing of specialised multiplexing hardware to automate the process.In general, the thesis shows that by careful conditioning of the electrochemical noise prior to analysis, many of the problems with the technique of impedance estimation from the electrochemical noise data can be overcome. It is shown that the electrochemical noise impedance estimation can be extended to encompass a time varying, frequency dependent quantity for studying dynamic systems; that phase information can be recovered from electrochemical noise for the purpose of constructing Nyquist impedance diagrams; and that asymmetric electrodes can be detected without requiring additional measurements.
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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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Dorosh, Anastasiia. "Direction-of-Arrival Estimation in Spherically Isotropic Noise." Thesis, Linnéuniversitetet, Institutionen för fysik och elektroteknik (IFE), 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-28603.

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Today the multisensor array signal processing of noisy measurements has received much attention. The classical problem in array signal processing is determining the location of an energy-radiating source relative to the location of the array, in other words, direction-of-arrival (DOA) estimation. One is considering the signal estimation problem when together with the signal(s) of interest some noise and interfering signals are present. In this report a direction-of-arrival estimation system is described based on an antenna array for detecting arrival angles in azimuth plane of signals pitched by the antenna array. For this, the Multiple Signal Classication (MUSIC) algorithmis first of all considered. Studies show that in spite of its good reputation and popularity among researches, it has a certain limit of its performance. In this subspace-based method for DOA estimation of signal wavefronts, the term corresponding to additive noise is initially assumed spatially white. In our paper, we address the problem of DOA estimation of multiple target signals in a particular noise situation - in correlated spherically isotropic noise, which, in many practical cases, models a more real context than under the white noise assumption. The purpose of this work is to analyze the behaviour of the MUSIC algorithm and compare its performance with some other algorithms (such as the Capon and the Classical algorithms) and, uppermost, to explore the quality of the detected angles in terms of precision depending on different parameters, e.g. number of samples, noise variance, number of incoming signals. Some modifications of the algorithms are also done is order to increase their performance. Program MATLAB is used to conduct the studies. The simulation results on the considered antenna array system indicate that in complex conditions the algorithms in question (and first of all, the MUSIC algorithm) are unable to automatically detect and localize the DOA signals with high accuracy. Other algorithms andways for simplification the problem (for example, procedure of denoising) exist and may provide more precision but require more computation time.
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Books on the topic "Estimation of the process noise"

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Identification of dynamical systems with small noise. Dordrecht: Kluwer Academic Publishers, 1994.

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Johnson, A. Process dynamics, estimation and control. Stevenage: Peregrinus on behalf of the Institution of Electrical Engineers, 1985.

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Engineers, Institution of Electrical, ed. Process dynamics estimation and control. London, U.K: P. Peregrinus Ltd. on behalf of the Institution of Electrical Engineers, 1985.

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Adithan, M. Process planning and cost estimation. New Delhi: New Age International (P) Ltd., Publishers, 2007.

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Kesavan, R. Process planning and cost estimation. 2nd ed. New Delhi: New Age International Ltd., 2009.

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Gershon, Eli. Advanced Topics in Control and Estimation of State-Multiplicative Noisy Systems. London: Springer London, 2013.

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Aït-Sahalia, Yacine. Ultra high frequency volatility estimation with dependent microstructure noise. Cambridge, MA: National Bureau of Economic Research, 2005.

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Aït-Sahalia, Yacine. Ultra high frequency volatility estimation with dependent microstructure noise. Cambridge, Mass: National Bureau of Economic Research, 2005.

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Ferguson, Ian. Dust and noise in the construction process. Sudbury: HSE Books, 1995.

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Wijers, B. J. Nonparametric estimation for a windowed line-segment process. Amsterdam, Netherlands: Centrum voor Wiskunde en Informatica, 1997.

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Book chapters on the topic "Estimation of the process noise"

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Ma, Hongbin, Liping Yan, Yuanqing Xia, and Mengyin Fu. "Kalman Filter with Recursive Process Noise Covariance Estimation." In Kalman Filtering and Information Fusion, 21–49. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-15-0806-6_3.

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Bulut, Yalcin, D. Vines-Cavanaugh, and Dionisio Bernal. "Process and Measurement Noise Estimation for Kalman Filtering." In Structural Dynamics, Volume 3, 375–86. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-9834-7_36.

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Tatsis, Konstantinos E., Vasilis K. Dertimanis, and Eleni N. Chatzi. "Adaptive Process and Measurement Noise Identification for Recursive Bayesian Estimation." In Model Validation and Uncertainty Quantification, Volume 3, 361–64. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-47638-0_39.

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Fujii, Tomohiro, and Masao Hirokawa. "A Data Concealing Technique with Random Noise Disturbance and a Restoring Technique for the Concealed Data by Stochastic Process Estimation." In International Symposium on Mathematics, Quantum Theory, and Cryptography, 103–24. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5191-8_11.

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Abstract We propose a technique to conceal data on a physical layer by disturbing them with some random noises, and moreover, a technique to restore the concealed data to the original ones by using the stochastic process estimation. Our concealing-restoring system manages the data on the physical layer from the data link layer. In addition to these proposals, we show the simulation result and some applications of our concealing-restoring technique.
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Muñoz-Gracia, M. P., J. Pages-Fita, and M. Marti-Recober. "Estimation of ARMA Process Parameters and Noise Variance by Means of a Non Linear Filtering Algorithm." In Compstat, 357–62. Heidelberg: Physica-Verlag HD, 1988. http://dx.doi.org/10.1007/978-3-642-46900-8_49.

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Westphal, Louis C. "State estimation in noise." In Handbook of Control Systems Engineering, 633–56. Boston, MA: Springer US, 2001. http://dx.doi.org/10.1007/978-1-4615-1533-3_28.

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Westphal, L. C. "State estimation in noise." In Sourcebook of Control Systems Engineering, 677–96. Boston, MA: Springer US, 1995. http://dx.doi.org/10.1007/978-1-4615-1805-1_28.

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Tang, Chongwu, Xiaokang Yang, and Guangtao Zhai. "Robust Noise Estimation Based on Noise Injection." In Advances in Multimedia Information Processing – PCM 2012, 142–52. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-34778-8_13.

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Réfrégier, Philippe. "Statistical Estimation." In Noise Theory and Application to Physics, 167–207. New York, NY: Springer New York, 2004. http://dx.doi.org/10.1007/978-0-387-22526-5_7.

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Corriou, Jean-Pierre. "Parametric Estimation Algorithms." In Process Control, 455–503. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-61143-3_12.

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Conference papers on the topic "Estimation of the process noise"

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Nguyen, H. N., and F. Guillemin. "On process noise covariance estimation." In 2017 25th Mediterranean Conference on Control and Automation (MED). IEEE, 2017. http://dx.doi.org/10.1109/med.2017.7984305.

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Lee, Cheol W. "Multirate Estimation for the Machining Process Under Multirate Noise." In ASME 2007 International Mechanical Engineering Congress and Exposition. ASMEDC, 2007. http://dx.doi.org/10.1115/imece2007-42187.

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Asynchronous measurement of process output characterizes a typical machining process in batch production. Various sensors are used for in-process measurement of process variables such as powers, forces, and vibration while the quality of parts and the tool conditions can be measured only by the postprocess inspection in most applications. The multirate scheme is being proposed as an efficient tool for integrating real-time sensor signals with postprocess inspection data for estimating immeasurable variables of the machining process. The machining process is subject to the process noise of varying frequencies including the within-cycle drift, cycle-to-cycle variation, and batch-to-batch variation. A multirate observer for simultaneous state and parameter estimation is built after the propagation of errors along a series of machining cycles is derived. Case studies with the grinding process demonstrate the efficacy of the developed algorithm.
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Ilhe, Paul, Francois Roueff, Eric Moulines, and Antoine Souloumiac. "Nonparametric estimation of a shot-noise process." In 2016 IEEE Statistical Signal Processing Workshop (SSP). IEEE, 2016. http://dx.doi.org/10.1109/ssp.2016.7551709.

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Zhao, Ye, Xiao He, and Donghua Zhou. "Fault estimation with biased process noise covariances." In 2017 36th Chinese Control Conference (CCC). IEEE, 2017. http://dx.doi.org/10.23919/chicc.2017.8028491.

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Hsu, Y. C., James Younger, and S. N. Balakrishnan. "Estimation of Process Noise Covariance in Homing Guidance." In 1991 American Control Conference. IEEE, 1991. http://dx.doi.org/10.23919/acc.1991.4791816.

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Guo, Ruocheng, Jundong Li, and Huan Liu. "INITIATOR: Noise-contrastive Estimation for Marked Temporal Point Process." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/303.

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Copious sequential event data has consistently increased in various high-impact domains such as social media and sharing economy. When events start to take place in a sequential fashion, an important question arises: "what type of event will happen at what time in the near future?" To answer the question, a class of mathematical models called the marked temporal point process is often exploited as it can model the timing and properties of events seamlessly in a joint framework. Recently, various recurrent neural network (RNN) models are proposed to enhance the predictive power of mark temporal point process. However, existing marked temporal point models are fundamentally based on the Maximum Likelihood Estimation (MLE) framework for the training, and inevitably suffer from the problem resulted from the intractable likelihood function. Surprisingly, little attention has been paid to address this issue. In this work, we propose INITIATOR - a novel training framework based on noise-contrastive estimation to resolve this problem. Theoretically, we show the exists a strong connection between the proposed INITIATOR and the exact MLE. Experimentally, the efficacy of INITIATOR is demonstrated over the state-of-the-art approaches on several real-world datasets from various areas.
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Lee, Cheol W. "Implementation of Multirate Estimation for the Cylindrical Grinding Process." In ASME 2008 International Manufacturing Science and Engineering Conference collocated with the 3rd JSME/ASME International Conference on Materials and Processing. ASMEDC, 2008. http://dx.doi.org/10.1115/msec_icmp2008-72307.

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This paper presents implementation results of the multirate estimation scheme, proposed by Lee (Lee, C.W., 2007, “Multirate Estimation for the Machining Process under Multirate Noise,” Proceedings of the 2007 ASME IMECE, November 11–15, 2007, Seattle, WA), on the cylindrical plunge grinding process. The multirate scheme is an efficient tool for integrating real-time sensor signals with postprocess inspection data for estimating the immeasurable variables. In order to accomplish this goal, process models for grinding power, surface roughness and wheel wear are developed using experimental data. Case studies are performed on simultaneous state-parameter estimation for actual grinding batches after the multirate observers are built based on the process models. Results from case studies validate the applicability of the proposed scheme to challenging estimation tasks in the manufacturing industry that cannot be undertaken by traditional approaches.
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Mason, Paul, and D. Mook. "A process noise covariance estimator." In Guidance, Navigation, and Control Conference. Reston, Virigina: American Institute of Aeronautics and Astronautics, 1994. http://dx.doi.org/10.2514/6.1994-3551.

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Miran, Sina, Jonathan Z. Simon, Michael C. Fu, Steven I. Marcus, and Behtash Babadi. "Estimation of State-Space Models with Gaussian Mixture Process Noise." In 2019 IEEE Data Science Workshop (DSW). IEEE, 2019. http://dx.doi.org/10.1109/dsw.2019.8755571.

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Lee, Kyuman, and Eric N. Johnson. "State estimation using Gaussian process regression for colored noise systems." In 2017 IEEE Aerospace Conference. IEEE, 2017. http://dx.doi.org/10.1109/aero.2017.7943781.

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Reports on the topic "Estimation of the process noise"

1

Chow, Winston C. Estimation Theory with Fractional Gaussian Noise. Fort Belvoir, VA: Defense Technical Information Center, September 1995. http://dx.doi.org/10.21236/ada301443.

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Shen, X., M. Bai, and S. Y. Lee. Noise estimation of beam position monitors at RHIC. Office of Scientific and Technical Information (OSTI), February 2014. http://dx.doi.org/10.2172/1121843.

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Quijano, Jorge E., Stan E. Dosso, Jan Dettmer, Lisa M. Zurk, and Martin Siderius. Bayesian Ambient Noise Inversion for Geoacoustic Uncertainty Estimation. Fort Belvoir, VA: Defense Technical Information Center, September 2011. http://dx.doi.org/10.21236/ada571872.

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Quijano, Jorge E., Stan E. Dosso, Jan Dettmer, Lisa M. Zurk, and Martin Siderius. Bayesian Ambient Noise Inversion for Geoacoustic Uncertainty Estimation. Fort Belvoir, VA: Defense Technical Information Center, September 2012. http://dx.doi.org/10.21236/ada575020.

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Wagstaff, Ronald A., and Joal J. Newcomb. Operation Rocky Road: Three-Dimensional Noise Field Directionality Estimation. Fort Belvoir, VA: Defense Technical Information Center, October 1995. http://dx.doi.org/10.21236/ada299769.

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Hodgkiss, William S. Source Signature Estimation and Noise Directionality in Shallow Water. Fort Belvoir, VA: Defense Technical Information Center, September 1995. http://dx.doi.org/10.21236/ada306524.

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Ait-Sahalia, Yacine, Per Mykland, and Lan Zhang. Ultra High Frequency Volatility Estimation with Dependent Microstructure Noise. Cambridge, MA: National Bureau of Economic Research, May 2005. http://dx.doi.org/10.3386/w11380.

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Loh, H. P., Jennifer Lyons, and Charles W. White. Process Equipment Cost Estimation, Final Report. Office of Scientific and Technical Information (OSTI), January 2002. http://dx.doi.org/10.2172/797810.

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Lin, Feng-Ling, and Ben H. Cantrell. Bounds on Doppler Frequency Estimation in Correlated Nonstationary Gaussian Noise. Fort Belvoir, VA: Defense Technical Information Center, August 1988. http://dx.doi.org/10.21236/ada201052.

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Speyer, Jason L. Estimation and Control for Linear Systems with Additive Cauchy Noise. Fort Belvoir, VA: Defense Technical Information Center, December 2013. http://dx.doi.org/10.21236/ada598079.

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