Kalman Filtering with Real-Time Applications

1. Front Matter

   Pages i-xv

   PDF

2. Preliminaries

   • Charles K. Chui, Guanrong Chen

   Pages 1-19

3. Kalman Filter: An Elementary Approach

   • Charles K. Chui, Guanrong Chen

   Pages 20-32

4. Orthogonal Projection and Kalman Filter

   • Charles K. Chui, Guanrong Chen

   Pages 33-49

5. Correlated System and Measurement Noise Processes

   • Charles K. Chui, Guanrong Chen

   Pages 50-67

6. Colored Noise

   • Charles K. Chui, Guanrong Chen

   Pages 68-77

7. Limiting Kalman Filter

   • Charles K. Chui, Guanrong Chen

   Pages 78-98

8. Sequential and Square-Root Algorithms

   • Charles K. Chui, Guanrong Chen

   Pages 99-110

9. Extended Kalman Filter and System Identification

   • Charles K. Chui, Guanrong Chen

   Pages 111-124

10. Decoupling of Filtering Equations

    • Charles K. Chui, Guanrong Chen

    Pages 125-136

11. Notes

    • Charles K. Chui, Guanrong Chen

    Pages 137-151

12. Back Matter

    Pages 153-191

    PDF

Kalman filtering is an optimal state estimation process applied to a dynamic system that involves random perturbations. More precisely, the Kalman filter gives a linear, unbiased, and min­ imum error variance recursive algorithm to optimally estimate the unknown state of a dynamic system from noisy data taken at discrete real-time intervals. It has been widely used in many areas of industrial and government applications such as video and laser tracking systems, satellite navigation, ballistic missile trajectory estimation, radar, and fue control. With the recent development of high-speed computers, the Kalman filter has become more use­ ful even for very complicated real-time applications. lnspite of its importance, the mathematical theory of Kalman filtering and its implications are not well understood even among many applied mathematicians and engineers. In fact, most prac­ titioners are just told what the filtering algorithms are without knowing why they work so well. One of the main objectives of this text is to disclose this mystery by presenting a fairly thor­ ough discussion of its mathematical theory and applications to various elementary real-time problems. A very elementary derivation of the filtering equations is fust presented. By assuming that certain matrices are nonsingular, the advantage of this approach is that the optimality of the Kalman filter can be easily understood. Of course these assump­ tions can be dropped by using the more well known method of orthogonal projection usually known as the innovations approach.

• Kalman filter
• accessible
• algebra
• algorithms
• filter
• filtering
• identification
• linear algebra
• mathematics
• measurement
• noise
• nonlinear system
• solution
• system identification
• systems theory

• Department of Mathematics and Department of Electrical Engineering, Texas A & M University, College Station, USA

  Charles K. Chui

• Department of Electrical and Computer Engineering, Rice University, Houston, USA

  Guanrong Chen

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