Kalman Filtering and Information Fusion

✍ Scribed by Hongbin Ma, Liping Yan, Yuanqing Xia, Mengyin Fu

Publisher: Springer Singapore
Year: 2020
Tongue: English
Leaves: 295
Edition: 1st ed. 2020
Category: Library

No coin nor oath required. For personal study only.

✦ Synopsis

This book addresses a key technology for digital information processing: Kalman filtering, which is generally considered to be one of the greatest discoveries of the 20th century. It introduces readers to issues concerning various uncertainties in a single plant, and to corresponding solutions based on adaptive estimation. Further, it discusses in detail the issues that arise when Kalman filtering technology is applied in multi-sensor systems and/or multi-agent systems, especially when various sensors are used in systems like intelligent robots, autonomous cars, smart homes, smart buildings, etc., requiring multi-sensor information fusion techniques. Furthermore, when multiple agents (subsystems) interact with one another, it produces coupling uncertainties, a challenging issue that is addressed here with the aid of novel decentralized adaptive filtering techniques.Overall, the book’s goal is to provide readers with a comprehensive investigation into the challenging problem of making Kalman filtering work well in the presence of various uncertainties and/or for multiple sensors/components. State-of-art techniques are introduced, together with a wealth of novel findings. As such, it can be a good reference book for researchers whose work involves filtering and applications; yet it can also serve as a postgraduate textbook for students in mathematics, engineering, automation, and related fields.To read this book, only a basic grasp of linear algebra and probability theory is needed, though experience with least squares, navigation, robotics, etc. would definitely be a plus.

✦ Table of Contents

Front Matter ....Pages i-xvii
Front Matter ....Pages 1-1
Introduction to Kalman Filtering (Hongbin Ma, Liping Yan, Yuanqing Xia, Mengyin Fu)....Pages 3-9
Challenges in Kalman Filtering (Hongbin Ma, Liping Yan, Yuanqing Xia, Mengyin Fu)....Pages 11-18
Front Matter ....Pages 19-19
Kalman Filter with Recursive Process Noise Covariance Estimation (Hongbin Ma, Liping Yan, Yuanqing Xia, Mengyin Fu)....Pages 21-49
Kalman Filter with Recursive Covariance Estimation Revisited with Technical Conditions Reduced (Hongbin Ma, Liping Yan, Yuanqing Xia, Mengyin Fu)....Pages 51-69
Modified Kalman Filter with Recursive Covariance Estimation for Gyroscope Denoising (Hongbin Ma, Liping Yan, Yuanqing Xia, Mengyin Fu)....Pages 71-94
Real-Time State Estimator Without Noise Covariance Matrices Knowledge (Hongbin Ma, Liping Yan, Yuanqing Xia, Mengyin Fu)....Pages 95-118
A Framework of Finite-Model Kalman Filter with Case Study: MVDP-FMKF Algorithm (Hongbin Ma, Liping Yan, Yuanqing Xia, Mengyin Fu)....Pages 119-146
Kalman Filters for Continuous Parametric Uncertain Systems (Hongbin Ma, Liping Yan, Yuanqing Xia, Mengyin Fu)....Pages 147-161
Front Matter ....Pages 163-163
Optimal Centralized, Recursive, and Distributed Fusion for Stochastic Systems with Coupled Noises (Hongbin Ma, Liping Yan, Yuanqing Xia, Mengyin Fu)....Pages 165-197
Optimal Estimation for Multirate Systems with Unreliable Measurements and Correlated Noise (Hongbin Ma, Liping Yan, Yuanqing Xia, Mengyin Fu)....Pages 199-222
CKF-Based State Estimation of Nonlinear System by Fusion of Multirate Multisensor Unreliable Measurements (Hongbin Ma, Liping Yan, Yuanqing Xia, Mengyin Fu)....Pages 223-238
Front Matter ....Pages 239-239
Decentralized Adaptive Filtering for Multi-agent Systems with Uncertain Couplings (Hongbin Ma, Liping Yan, Yuanqing Xia, Mengyin Fu)....Pages 241-271
Comparison of Several Filtering Methods for Linear Multi-agent Systems with Local Unknown Parametric Couplings (Hongbin Ma, Liping Yan, Yuanqing Xia, Mengyin Fu)....Pages 273-291

✦ Subjects

Engineering; Control, Robotics, Mechatronics; Mathematical and Computational Engineering; Systems Theory, Control; Electrical Engineering

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