DocumentCode :
1304248
Title :
A new recursive filter for systems with multiplicative noise
Author :
Chow, B.S. ; Birkemeier, W.P.
Author_Institution :
Dept. of Electr. Eng., Nat. Sun Yat-Sen Univ., Kaohsiung, Taiwan
Volume :
36
Issue :
6
fYear :
1990
fDate :
11/1/1990 12:00:00 AM
Firstpage :
1430
Lastpage :
1435
Abstract :
An optimal linear recursive minimum mean-square-error estimator was previously developed by the authors (see IEEE Trans. Autom. Control, vol.34, no.5, p.568-74, May 1989) for a zero-mean signal corrupted by multiplicative noise in its measurement model. This recursive filter cannot be obtained by the recursive structure of a conventional Kalman filter where the new estimate is a linear combination of the previous estimate and the new data. Instead, the recursive structure was achieved by combining the previous estimate with recursive innovation, a linear combination of the most recent two data samples and the previous estimate. In this work the signal is extended to be nonzero-mean. In the conventional Kalman filter, the superposition principle can be applied to both the signal and the measurement models for this nonzero-mean extension. However, when multiplicative noise exists, the measurement model becomes nonlinear. Therefore, a new recursive structure for the innovation process needs to be developed to achieve a recursive filter
Keywords :
filtering and prediction theory; interference (signal); signal processing; Kalman filter; MMSE estimator; minimum mean-square-error estimator; multiplicative noise; nonlinear measurement mode; nonzero-mean signal; optimal linear recursive estimator; recursive filter; recursive innovation; zero-mean signal; Additive noise; Kalman filters; Noise level; Noise measurement; Nonlinear filters; Recursive estimation; Signal processing; Springs; Sun; Technological innovation;
fLanguage :
English
Journal_Title :
Information Theory, IEEE Transactions on
Publisher :
ieee
ISSN :
0018-9448
Type :
jour
DOI :
10.1109/18.59939
Filename :
59939
Link To Document :
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