Approximate Kalman filtering

✍ Scribed by Chen, Quanrong (eds)

• Publisher: World Scientific Pub Co Inc
• Year: 1993
• Tongue: English
• Leaves: 227
• Series: Series in approximations and decompositions vol. 2
• Category: Library

✦ Synopsis

Kalman filtering algorithm gives optimal (linear, unbiased and minimum error-variance) estimates of the unknown state vectors of a linear dynamic-observation system, under the regular conditions such as perfect data information; complete noise statistics; exact linear modelling; ideal will-conditioned matrices in computation and strictly centralized filtering. In practice, however, one or more of the aforementioned conditions may not be satisfied, so that the standard Kalman filtering algorithm cannot be directly used, and hence "approximate Kalman filtering" becomes necessary. In the last decade, a great deal of attention has been focused on modifying and/or extending the standard Kalman filtering technique to handle such irregular cases. This book is a collection of several survey articles summarizing recent contributions to the field, along the line of approximate Kalman filtering with emphasis on its practical aspects

✦ Table of Contents

Content: I. Extended Kalman Filtering for Nonlinear Systems. Extended Kalman Filters 1: Continuous and Discrete Linearizations / T.E. Bullock and M.J. Moorman. Extended Kalman Filters 2: Standard, Modified and Ideal / T.E. Bullock and M.J. Moorman. Extended Kalman Filters 3: A Mathematical Analysis of Bias / M.J. Moorman and T.E. Bullock --
II. Initialization of Kalman Filtering. Fisher Initialization in the Presence of Ill-Conditioned Measurements / D. Catlin. Initializing the Kalman Filter with Incompletely Specified Initial Conditions / V. Gomez and A. Maravall --
III. Adaptive Kalman Filtering in Irregular Environments. Robust Adaptive Kalman Filtering / A.R. Moghaddamjoo and R.L. Kirlin. On-line Estimation of Signal and Noise Parameters and the Adaptive Kalman Filtering / P.J. Wojcik. Suboptimal Kalman Filtering for Linear Systems with Non-Gaussian Noise / H. Wu and G. Chen --
IV. Set-valued and Distributed Kalman Filtering. Set-valued Kalman Filtering / D. Morrell and W.C. Stirling. Distributed Filtering Using Set Models for Systems with Non-Gaussian Noise / L. Hong --
V. Stability Analysis and Numerical Approximation of Kalman Filtering. Robust Stability Analysis of Kalman Filter under Parametric and Noise Uncertainties / B.S. Chen and S.C. Peng. Numerical Approximations and Other Structural Issues in Practical Implementations of Kalman Filtering / T.H. Kerr.