An introduction to kalman filtering with MATLAB examples

被引：0

作者：

Kovvali, Narayan ^{[1
]}

Banavar, Mahesh ^{[1
]}

Spanias, Andreas ^{[1
]}

机构：

[1] SenSIP Center, Arizona State University

来源：

Synthesis Lectures on Signal Processing | 2013年 / 12卷

关键词：

dynamical system; Gaussian noise; Kalman filter; linearity; parameter estimation; sequential Bayesian estimation; state space model; tracking;

D O I：

10.2200/S00534ED1V01Y201309SPR012

中图分类号：

学科分类号：

摘要：

Download Free Sample The Kalman filter is the Bayesian optimum solution to the problem of sequentially estimating the states of a dynamical system in which the state evolution and measurement processes are both linear and Gaussian. Given the ubiquity of such systems, the Kalman filter finds use in a variety of applications, e.g., target tracking, guidance and navigation, and communications systems. The purpose of this book is to present a brief introduction to Kalman filtering. The theoretical framework of the Kalman filter is first presented, followed by examples showing its use in practical applications. Extensions of the method to nonlinear problems and distributed applications are discussed. A software implementation of the algorithm in the MATLAB programming language is provided, as well as MATLAB code for several example applications discussed in the manuscript. Authors' Biographies Copyright © 2013 by Morgan & Claypool.

引用

页码：1 / 79

页数：78

共 40 条

[1]

Trees H.L.V., Detection, Estimation, and Modulation Theory, Part I., (2001)

[2]

Papoulis A., Pillai S.U., Probability, Random Variables and Stochastic Processes, 4th Ed, (2002)

[3]

Kalman R., A new approach to linear filtering and prediction problems, Trans. ASME Ser. D. J. Basic Eng., 82, pp. 35-45, (1960)

[4]

Kalman R., Bucy R., New results in linear filtering and prediction theory, Trans. ASME Ser. D. J. Basic Eng., 83, pp. 95-108, (1961)

[5]

Ristic B., Arulampalam S., Gordon N., Beyond the Kalman Filter: Particle Filters for Tracking Applications, (2004)

[6]

Bar-Shalom Y., Li X.R., Kirubarajan T., Estimation with Applications to Tracking and Navigation, 1st Ed, (2001)

[7]

Cipra B., Engineers look to Kalman filtering for guidance, SIAM News, 26, (1993)

[8]

Grewal M.S., Weill L.R., Andrews A.P., Global Positioning Systems, Inertial Navigation, and Integration, (2001)

[9]

Titterton D., Weston J., Strapdown Inertial Navigation Technology, 2nd Ed, (2004)

[10]

Duda R.O., Hart P.E., Stork D.G., Pattern Classification, 2nd Ed, (2001)

← 1 2 3 4 →