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Nonrecursive and recursive methods for parameter estimation in filtering problemsFormat | Member Price | Non-Member Price |
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Paper 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.
Paper Details
Date Published: 25 August 2003
PDF: 12 pages
Proc. SPIE 5096, Signal Processing, Sensor Fusion, and Target Recognition XII, (25 August 2003); doi: 10.1117/12.488024
Published in SPIE Proceedings Vol. 5096:
Signal Processing, Sensor Fusion, and Target Recognition XII
Ivan Kadar, Editor(s)
PDF: 12 pages
Proc. SPIE 5096, Signal Processing, Sensor Fusion, and Target Recognition XII, (25 August 2003); doi: 10.1117/12.488024
Show Author Affiliations
Michael A. Kouritzin, Univ. of Alberta (Canada)
Hongwei Long, Univ. of Alberta (Canada)
Hongwei Long, Univ. of Alberta (Canada)
Published in SPIE Proceedings Vol. 5096:
Signal Processing, Sensor Fusion, and Target Recognition XII
Ivan Kadar, Editor(s)
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