DocumentCode :
1150088
Title :
Algorithms for Discrete Sequential Maximum Likelihood Bias Estimation and Associated Error Analysis
Author :
Lin, Jin L. ; Sage, Andrew P.
Issue :
4
fYear :
1971
Firstpage :
314
Lastpage :
324
Abstract :
Optimization theory and discrete invariant imbedding is used in order to derive computationally efficient sequential algorithms for the maximum likelihood estimation of bias errors in linear discrete recursive filtering with noise corrupted input observations and correlated plant and measurement noise. Error analysis algorithms are derived for adaptive and nonadaptive systems with bias and modeling errors. Examples demonstrate the efficiency of the adaptive estimation algorithms and the error analysis algorithms for estimation with bias uncertainty.
Keywords :
Adaptive estimation; Adaptive systems; Error analysis; Estimation error; Filtering algorithms; Filtering theory; Maximum likelihood estimation; Noise measurement; Nonlinear filters; Recursive estimation;
fLanguage :
English
Journal_Title :
Systems, Man and Cybernetics, IEEE Transactions on
Publisher :
ieee
ISSN :
0018-9472
Type :
jour
DOI :
10.1109/TSMC.1971.4308313
Filename :
4308313
Link To Document :
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