Ebook: Kalman Filtering: with Real-Time Applications

Kalman Filtering with Real-Time Applications presents a thorough discussion of the mathematical theory and computational schemes of Kalman filtering. The filtering algorithms are derived via different approaches, including a direct method consisting of a series of elementary steps, and an indirect method based on innovation projection. Other topics include Kalman filtering for systems with correlated noise or colored noise, limiting Kalman filtering for time-invariant systems, extended Kalman filtering for nonlinear systems, interval Kalman filtering for uncertain systems, and wavelet Kalman filtering for multiresolution analysis of random signals. Most filtering algorithms are illustrated by using simplified radar tracking examples. The style of the book is informal, and the mathematics is elementary but rigorous. The text is self-contained, suitable for self-study, and accessible to all readers with a minimum knowledge of linear algebra, probability theory, and system engineering.

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Kalman Filtering with Real-Time Applications presents a thorough discussion of the mathematical theory and computational schemes of Kalman filtering. The filtering algorithms are derived via different approaches, including a direct method consisting of a series of elementary steps, and an indirect method based on innovation projection. Other topics include Kalman filtering for systems with correlated noise or colored noise, limiting Kalman filtering for time-invariant systems, extended Kalman filtering for nonlinear systems, interval Kalman filtering for uncertain systems, and wavelet Kalman filtering for multiresolution analysis of random signals. Most filtering algorithms are illustrated by using simplified radar tracking examples. The style of the book is informal, and the mathematics is elementary but rigorous. The text is self-contained, suitable for self-study, and accessible to all readers with a minimum knowledge of linear algebra, probability theory, and system engineering.

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Kalman Filtering with Real-Time Applications presents a thorough discussion of the mathematical theory and computational schemes of Kalman filtering. The filtering algorithms are derived via different approaches, including a direct method consisting of a series of elementary steps, and an indirect method based on innovation projection. Other topics include Kalman filtering for systems with correlated noise or colored noise, limiting Kalman filtering for time-invariant systems, extended Kalman filtering for nonlinear systems, interval Kalman filtering for uncertain systems, and wavelet Kalman filtering for multiresolution analysis of random signals. Most filtering algorithms are illustrated by using simplified radar tracking examples. The style of the book is informal, and the mathematics is elementary but rigorous. The text is self-contained, suitable for self-study, and accessible to all readers with a minimum knowledge of linear algebra, probability theory, and system engineering.
Content:
Front Matter....Pages I-XIV
Preliminaries....Pages 1-19
Kalman Filter: An Elementary Approach....Pages 20-32
Orthogonal Projection and Kalman Filter....Pages 33-48
Correlated System and Measurement Noise Processes....Pages 49-66
Colored Noise....Pages 67-76
Limiting Kalman Filter....Pages 77-96
Sequential and Square-Root Algorithms....Pages 97-107
Extended Kalman Filter and System Identification....Pages 108-130
Decoupling of Filtering Equations....Pages 131-142
Kalman Filtering for Interval Systems....Pages 143-163
Wavelet Kalman Filtering....Pages 164-177
Notes....Pages 178-190
Back Matter....Pages 191-229

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Kalman Filtering with Real-Time Applications presents a thorough discussion of the mathematical theory and computational schemes of Kalman filtering. The filtering algorithms are derived via different approaches, including a direct method consisting of a series of elementary steps, and an indirect method based on innovation projection. Other topics include Kalman filtering for systems with correlated noise or colored noise, limiting Kalman filtering for time-invariant systems, extended Kalman filtering for nonlinear systems, interval Kalman filtering for uncertain systems, and wavelet Kalman filtering for multiresolution analysis of random signals. Most filtering algorithms are illustrated by using simplified radar tracking examples. The style of the book is informal, and the mathematics is elementary but rigorous. The text is self-contained, suitable for self-study, and accessible to all readers with a minimum knowledge of linear algebra, probability theory, and system engineering.
Content:
Front Matter....Pages I-XIV
Preliminaries....Pages 1-19
Kalman Filter: An Elementary Approach....Pages 20-32
Orthogonal Projection and Kalman Filter....Pages 33-48
Correlated System and Measurement Noise Processes....Pages 49-66
Colored Noise....Pages 67-76
Limiting Kalman Filter....Pages 77-96
Sequential and Square-Root Algorithms....Pages 97-107
Extended Kalman Filter and System Identification....Pages 108-130
Decoupling of Filtering Equations....Pages 131-142
Kalman Filtering for Interval Systems....Pages 143-163
Wavelet Kalman Filtering....Pages 164-177
Notes....Pages 178-190
Back Matter....Pages 191-229
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