Extended kalman

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  • The discussion turns now to what might be called Kalman ®lter engineering, which is that body of applicable knowledge that has evolved through practical experience in the use and misuse of the Kalman ®lter. The material of the previous two chapters (extended Kalman ®ltering and square-root ®ltering) has also evolved in this way and is part of the same general subject.

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  • Hội thảo toàn quốc về Điện tử - Truyền thông – An toàn thông tin, ATC/REV-2012 Multi-sensor mobile robot and the sensor fusion-based localization with Extended Kalman Filter Trần Thuận Hoàng, Phùng Mạnh Dương, Đặng Anh Việt và Trần Quang Vinh Trường Đại học Công nghệ, Đại học Quốc gia Hà nội e-Mail: thuanhoang@donga.edu.

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  • The Extended Kalman Filter (EKF) provides an efficient method for generating approximate maximum-likelihood estimates of the state of a discrete-time nonlinear dynamical system (see Chapter 1). The filter involves a recursive procedure to optimally combine noisy observations with predictions from the known dynamic model. A second use of the EKF involves estimating the parameters of a model (e.g., neural network) given clean training data of input and output data (see Chapter 2).

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  • In Chapter 2, Puskorius and Feldkamp described a procedure for the supervised training of a recurrent multilayer perceptron – the nodedecoupled extended Kalman filter (NDEKF) algorithm. We now use this model to deal with high-dimensional signals: moving visual images. Many complexities arise in visual processing that are not present in onedimensional prediction problems: the scene may be cluttered with backKalman Filtering and Neural Network

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  • In this chapter, we consider another application of the extended Kalman filter recurrent multilayer perceptron (EKF-RMLP) scheme: the modeling of a chaotic time series or one that could be potentially chaotic. The generation of a chaotic process is governed by a coupled set of nonlinear differential or difference equations.

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  • In this book, the extended Kalman filter (EKF) has been used as the standard technique for performing recursive nonlinear estimation. The EKF algorithm, however, provides only an approximation to optimal nonlinear estimation. In this chapter, we point out the underlying assumptions and flaws in the EKF, and present an alternative filter with performance superior to that of the EKF. This algorithm, referred to as the unscented Kalman filter (UKF), was first proposed by Julier et al. [1–3], and further developed by Wan and van der Merwe [4–7]....

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  • NONLINEAR OBSERVATION SCHEME AND DYNAMIC MODEL (EXTENDED KALMAN FILTER) 16.1 INTRODUCTION In this section we extend the results for the linear time-invariant and timevariant cases to where the observations are nonlinearly related to the state vector and/or the target dynamics model is a nonlinear relationship [5, pp. 105– 111, 166–171, 298–300]. The approachs involve the use of linearization procedures. This linearization allows us to apply the linear least-squares and minimum-variance theory results obtained so far.

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  • THE UNSCENTED KALMAN FILTER Eric A. Wan and Rudolph van der Merwe Department of Electrical and Computer Engineering, Oregon Graduate Institute of Science and Technology, Beaverton, Oregon, U.S.A. 7.1 INTRODUCTION In this book, the extended Kalman filter (EKF) has been used as the standard technique for performing recursive nonlinear estimation. The EKF algorithm, however, provides only an approximation to optimal nonlinear estimation.

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  • CHAOTIC DYNAMICS Gaurav S. Patel Department of Electrical and Computer Engineering, McMaster University, Hamilton, Ontario, Canada Simon Haykin Communications Research Laboratory, McMaster University, Hamilton, Ontario, Canada (haykin@mcmaster.ca) 4.1 INTRODUCTION In this chapter, we consider another application of the extended Kalman filter recurrent multilayer perceptron (EKF-RMLP) scheme: the modeling of a chaotic time series or one that could be potentially chaotic. The generation of a chaotic process is governed by a coupled set of nonlinear differential or difference equations.

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  • DUAL EXTENDED KALMAN FILTER METHODS Eric A. Wan and Alex T. Nelson Department of Electrical and Computer Engineering, Oregon Graduate Institute of Science and Technology, Beaverton, Oregon, U.S.A. 5.1 INTRODUCTION The Extended Kalman Filter (EKF) provides an efficient method for generating approximate maximum-likelihood estimates of the state of a discrete-time nonlinear dynamical system (see Chapter 1). The filter involves a recursive procedure to optimally combine noisy observations with predictions from the known dynamic model.

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  • LEARNING SHAPE AND MOTION FROM IMAGE SEQUENCES Gaurav S. Patel Department of Electrical and Computer Engineering, McMaster University, Hamilton, Ontario, Canada Sue Becker and Ron Racine Department of Psychology, McMaster University, Hamilton, Ontario, Canada (beckers@mcmaster.ca) 3.1 INTRODUCTION In Chapter 2, Puskorius and Feldkamp described a procedure for the supervised training of a recurrent multilayer perceptron – the nodedecoupled extended Kalman filter (NDEKF) algorithm. We now use this model to deal with high-dimensional signals: moving visual images.

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  • The discussion about the manned spacecraft program was initiated at NASA in 1959. Only one year later, Dr. Kalman and Dr. Schmidt linked the linear Kalman filter and the perturbation theory in order to obtain the Kalman-Schmidt filter, currently known as the extended Kalman filter. This approach would be implemented in 1961 using an IBM 704 computer (running at approximately 4000 operations per second) for simulation purposes, and subsequently, in July 1969, for making the descent of the Apollo 11 lunar module to the Moon possible....

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  • LINEAR TIME-VARIANT SYSTEM 15.1 INTRODUCTION In this chapter we extend the results of Chapters 4 and 8 to systems having time-variant dynamic models and observation schemes [5, pp. 99–104]. For a time-varying observation system, the observation matrix M of (4.1-1) and (4.1-5) could be different at different times, that is, for different n. Thus the observation equation becomes Y n ¼ M nX n þ N n ð15:1-1Þ For a time-varying dynamics model the transition matrix È would be different at different times. In this case È of (8.

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  • Introduction Least-Squares Estimation Properties of Estimators Best Linear Unbiased Estimation Maximum-Likelihood Estimation Mean-Squared Estimation of Random Parameters Maximum A Posteriori Estimation of Random Parameters The Basic State-Variable Model State Estimation for the Basic State-Variable Model Prediction • Filtering (the Kalman Filter) • Smoothing Jerry M. Mendel University of Southern California 15.10 Digital Wiener Filtering 15.11 Linear Prediction in DSP, and Kalman Filtering 15.12 Iterated Least Squares 15.

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  • EURASIP Journal on Applied Signal Processing 2003:13, 1268–1278 c 2003 Hindawi Publishing Corporation Extended Kalman Filter Channel Estimation for Line-of-Sight Detection in WCDMA Mobile Positioning Abdelmonaem Lakhzouri Institute of Communications Engineering, Tampere University of Technology, P.O. Box 553, 33101 Tampere, Finland Email: abdelmonaem.lakhzouri@tut.fi Elena Simona Lohan Institute of Communications Engineering, Tampere University of Technology, P.O. Box 553, 33101 Tampere, Finland Email: elena-simona.lohan@tut.

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  • Bài báo này đề cập đến vấn đề ước lượng trễ khi có pha đinh đa đường trong hệ thống CDMA. Phương pháp mới được đưa ra là sử dụng lọc UKF (Unscented Kalman Filter) để ước lượng trễ và các hệ số pha đinh đa đường của tín hiệu trong hệ thống CDMA khi mô hình kênh được giả thiết là đường trễ nhánh. So với thuật toán EKF (Extended Kalman Filter), khi sử dụng thuật toán UKF, độ phức tạp tính toán giảm đi đáng kể.

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