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Signal processing

About: Signal processing is a(n) research topic. Over the lifetime, 73467 publication(s) have been published within this topic receiving 983533 citation(s). more


Open accessJournal ArticleDOI: 10.1109/78.978374
Abstract: Increasingly, for many application areas, it is becoming important to include elements of nonlinearity and non-Gaussianity in order to model accurately the underlying dynamics of a physical system. Moreover, it is typically crucial to process data on-line as it arrives, both from the point of view of storage costs as well as for rapid adaptation to changing signal characteristics. In this paper, we review both optimal and suboptimal Bayesian algorithms for nonlinear/non-Gaussian tracking problems, with a focus on particle filters. Particle filters are sequential Monte Carlo methods based on point mass (or "particle") representations of probability densities, which can be applied to any state-space model and which generalize the traditional Kalman filtering methods. Several variants of the particle filter such as SIR, ASIR, and RPF are introduced within a generic framework of the sequential importance sampling (SIS) algorithm. These are discussed and compared with the standard EKF through an illustrative example. more

  • Fig. 4. Evolution of the upper and lower2 positions of the state as estimated by the EKF (dotted) with the true state also shown (solid).
    Fig. 4. Evolution of the upper and lower2 positions of the state as estimated by the EKF (dotted) with the true state also shown (solid).
  • Fig. 5. Image representing evolution of probability density for approximate grid-based filter.
    Fig. 5. Image representing evolution of probability density for approximate grid-based filter.
  • Fig. 3. Evolution of the EKFs mean estimate of the state.
    Fig. 3. Evolution of the EKFs mean estimate of the state.
  • Fig. 9. Image representing evolution of probability density for “likelihood” particle filter.
    Fig. 9. Image representing evolution of probability density for “likelihood” particle filter.
Topics: Particle filter (67%), Auxiliary particle filter (63%), Monte Carlo localization (59%) more

10,977 Citations

Open accessBook
01 Jan 1989-
Abstract: For senior/graduate-level courses in Discrete-Time Signal Processing. THE definitive, authoritative text on DSP -- ideal for those with an introductory-level knowledge of signals and systems. Written by prominent, DSP pioneers, it provides thorough treatment of the fundamental theorems and properties of discrete-time linear systems, filtering, sampling, and discrete-time Fourier Analysis. By focusing on the general and universal concepts in discrete-time signal processing, it remains vital and relevant to the new challenges arising in the field --without limiting itself to specific technologies with relatively short life spans. more

10,383 Citations

Journal ArticleDOI: 10.1109/29.32276
R. Roy1, Thomas Kailath1Institutions (1)
Abstract: An approach to the general problem of signal parameter estimation is described. The algorithm differs from its predecessor in that a total least-squares rather than a standard least-squares criterion is used. Although discussed in the context of direction-of-arrival estimation, ESPRIT can be applied to a wide variety of problems including accurate detection and estimation of sinusoids in noise. It exploits an underlying rotational invariance among signal subspaces induced by an array of sensors with a translational invariance structure. The technique, when applicable, manifests significant performance and computational advantages over previous algorithms such as MEM, Capon's MLM, and MUSIC. > more

5,794 Citations

Open accessBook
Bernard Widrow1, Samuel D. Stearns2Institutions (2)
01 Jan 1985-
Abstract: GENERAL INTRODUCTION. Adaptive Systems. The Adaptive Linear Combiner. THEORY OF ADAPTATION WITH STATIONARY SIGNALS. Properties of the Quadratic Performance Surface. Searching the Performance Surface. Gradient Estimation and Its Effects on Adaptation. ADAPTIVE ALGORITHMS AND STRUCTURES. The LMS Algorithm. The Z-Transform in Adaptive Signal Processing. Other Adaptive Algorithms and Structures. Adaptive Lattice Filters. APPLICATIONS. Adaptive Modeling and System Identification. Inverse Adaptive Modeling, Deconvolution, and Equalization. Adaptive Control Systems. Adaptive Interference Cancelling. Introduction to Adaptive Arrays and Adaptive Beamforming. Analysis of Adaptive Beamformers. more

Topics: Adaptive filter (67%), Adaptive beamformer (67%), Adaptive control (64%) more

5,644 Citations

Open accessJournal Article
Abstract: Estimation theory has had a tremendous impact on many problem areas over the past two decades. Beginning with its original use in the aerospace industry, its applications can now be found in many different areas such as control and communjcations, power systems, transportation systems, bioengineering, image processing, etc. Along with linear system theory and optimal control, a course in estimation theorycan be found in the graduate system and control curriculum,of most schools in the country. In fact, it is probably one of the most,salable courses as far as employment is concerned. However, despite its economic value and the amount of activities in the field, very few books on estimation theory have appeared recently. This book helps to fill the void in the market and does that in a superb manner. Although the book is called OptimalFiltering, the coverage is restricted to discrete time filtering. A more appropriate title would thus be Optimal Discrete Time ,Filtering. The authors’ decision to concentrate on discrete time f lters is due to “recent technological developments as well as the easier path offered students and instructors.” This is probably a wise move since a thorough treatment of continuous time filtering will require a better knowledge o f stochastic processes than most graduate students or engineers will have. As it stands now, the text requires little background beyond that of linear system theory and probability theory. Written by active researchers, in the area, the book covers the standard topics such as Kalman filtering, innovations processes, smoothing, and adaptive and nonlinear estimation. Much of the material in the book has been around for a long time and has been widely used, by practitioners in the area: Some results are more recent. However,-it .has been difficult to locate all of them presented in a n organized manner within a single text. This is especially true of the chapters dealing with the computation aspects and nonlinear and adaptive estimation. After a short introductory chapter, Chapter 2 introduces the mathematical model to be used throughout most of the book. The discrete time Kalman filter is 1 hen presented in Chapter 3, along with some applications. Chapter 4 contains a treatment more

Topics: Filtering problem (68%), Extended Kalman filter (67%), Kalman filter (60%) more

4,836 Citations

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Topic's top 5 most impactful authors

Tetsujiro Kondo

37 papers, 178 citations

Yonina C. Eldar

29 papers, 2.1K citations

Ljubisa Stankovic

28 papers, 1K citations

Srdjan Stankovic

26 papers, 523 citations

Petre Stoica

26 papers, 1.7K citations

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