Nonrecursive and recursive methods for parameter estimation in filtering problems

Michael A. Kouritzin; Hongwei Long; Xinjian Ma; Wei Sun

doi:10.1117/12.488024

25 August 2003 Nonrecursive and recursive methods for parameter estimation in filtering problems

Michael A. Kouritzin, Hongwei Long, Xinjian Ma, Wei Sun

Proceedings Volume 5096, Signal Processing, Sensor Fusion, and Target Recognition XII; (2003) https://doi.org/10.1117/12.488024
Event: AeroSense 2003, 2003, Orlando, Florida, United States

Abstract

Nonlinear filtering is an important and effective tool for handling estimation of signals when observations are incomplete, distorted, and corrupted. Quite often in real world applications, the signals to be estimated contain unknown parameters which need to be determined. Herein, we develop and analyze non-recursive and recursive methods, which can deal with combined state and parameter estimation for nonlinear partially-observed stochastic systems. For the non-recursive method, we obtain the unknown parameters through solving a system of non-singular finite order linear equations. For the recursive method, we generalize the least squares method and develop a particle prediction error identification algorithm so that it can be applied to general nonlinear stochastic systems. We use the branching particle filter to do the signal state estimation and implement simulations for both methods.

Citation Download Citation

Michael A. Kouritzin, Hongwei Long, Xinjian Ma, and Wei Sun "Nonrecursive and recursive methods for parameter estimation in filtering problems", Proc. SPIE 5096, Signal Processing, Sensor Fusion, and Target Recognition XII, (25 August 2003); https://doi.org/10.1117/12.488024

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