Kalman filter

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  • The aim of this book is to provide an overview of recent developments in Kalman filter theory and their applications in engineering and scientific fields. The book is divided into 24 chapters and organized in five blocks corresponding to recent advances in Kalman filtering theory, applications in medical and biological sciences, tracking and positioning systems, electrical engineering and, finally, industrial processes and communication networks.

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  • Tham khảo sách 'kalman filter recent advances and applications_1', công nghệ thông tin, kỹ thuật lập trình phục vụ nhu cầu học tập, nghiên cứu và làm việc hiệu quả

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  • The first objective of Kalman filtering With a radar tracking implementation is to give deep enough insight into the mathematics of the Kalman filter algorithm to be able to choose the correct type of algorithm and to set all the parameters correctly in a basic application. This description also includes several examples of different approaches to derive and to explain the Kalman filter algorithm.

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  • Theoretically the Kalman Filter is an estimator for what is called the linear-quadratic problem, which is the problem of estimating the instantaneous ``state'' (a concept that will be made more precise in the next chapter) of a linear dynamic system perturbed by white noiseÐby using measurements linearly related to the state but corrupted by white noise. The resulting estimator is statistically optimal with respect to any quadratic function of estimation error

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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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  • Digital filters are widely used in processing digital signals of many diverse applications, including speech processing and data communications, image and video processing, sonar, radar, seismic and oil exploration, and consumer electronics.

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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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  • KALMAN FILTER 2.1 TWO-STATE KALMAN FILTER Up to now we have used a deterministic description for the target motion. Specifically, we have assumed a target having a constant-velocity motion as given by _ x nþ1 ¼ x n þ T x n _ _ x nþ1 ¼ x n ð1:1-1aÞ ð1:1-1bÞ In the real world the target will not have a constant velocity for all time. There is actually uncertainty in the target trajectory, the target accelerating or turning at any given time. Kalman allowed for this uncertainty in the target motion by adding a random component to the target dynamics [19,...

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  • Although the rediscovery in the mid 1980s of the backpropagation algorithm by Rumelhart, Hinton, and Williams [1] has long been viewed as a landmark event in the history of neural network computing and has led to a sustained resurgence of activity, the relative ineffectiveness of this simple gradient method has motivated many researchers to develop enhanced training procedures. In fact, the neural network literature has been inundated with papers proposing alternative training Kalman Filtering and Neural Networks...

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  • KALMAN FILTER REVISITED 18.1 INTRODUCTION In Section 2.6 we developed the Kalman filter as the minimization of a quadratic error function. In Chapter 9 we developed the Kalman filter from the minimum variance estimate for the case where there is no driving noise present in the target dynamics model. In this chapter we develop the Kalman filter for more general case [5, pp. 603–618]. The concept of the Kalman filter as a fading-memory filter shall be presented. Also its use for eliminating bias error buildup will be presented.

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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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  • LEARNING NONLINEAR DYNAMICAL SYSTEMS USING THE EXPECTATION– MAXIMIZATION ALGORITHM Sam Roweis and Zoubin Ghahramani Gatsby Computational Neuroscience Unit, University College London, London U.K. (zoubin@gatsby.ucl.ac.uk) 6.1 LEARNING STOCHASTIC NONLINEAR DYNAMICS Since the advent of cybernetics, dynamical systems have been an important modeling tool in fields ranging from engineering to the physical and social sciences. Most realistic dynamical systems models have two essential features.

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  • KALMAN FILTERS Simon Haykin Communications Research Laboratory, McMaster University, Hamilton, Ontario, Canada (haykin@mcmaster.ca) 1.1 INTRODUCTION The celebrated Kalman filter, rooted in the state-space formulation of linear dynamical systems, provides a recursive solution to the linear optimal filtering problem. It applies to stationary as well as nonstationary environments. The solution is recursive in that each updated estimate of the state is computed from the previous estimate and the new input data, so only the previous estimate requires storage.

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  • PARAMETER-BASED KALMAN FILTER TRAINING: THEORY AND IMPLEMENTATION Gintaras V. Puskorius and Lee A. Feldkamp Ford Research Laboratory, Ford Motor Company, Dearborn, Michigan, U.S.A. (gpuskori@ford.com, lfeldkam@ford.com) 2.

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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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  • Tham khảo tài liệu 'forecasting, structural time series models and the kalman filter by a. c. harvey_4', tài chính - ngân hàng, tài chính doanh nghiệp phục vụ nhu cầu học tập, nghiên cứu và làm việc hiệu quả

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  • Tham khảo tài liệu 'forecasting, structural time series models and the kalman filter by a. c. harvey_3', tài chính - ngân hàng, tài chính doanh nghiệp phục vụ nhu cầu học tập, nghiên cứu và làm việc hiệu quả

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