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L p ‐stability of estimation errors of kalman filter for tracking time‐varying parameters
Author(s) -
Zhang J. F.,
Guo L.,
Chen H. F.
Publication year - 1991
Publication title -
international journal of adaptive control and signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.73
H-Index - 66
eISSN - 1099-1115
pISSN - 0890-6327
DOI - 10.1002/acs.4480050302
Subject(s) - kalman filter , control theory (sociology) , stability (learning theory) , context (archaeology) , tracking (education) , mathematics , filter (signal processing) , extended kalman filter , alpha beta filter , state space , computer science , state vector , artificial intelligence , statistics , moving horizon estimation , control (management) , machine learning , psychology , paleontology , pedagogy , computer vision , biology , physics , classical mechanics
The Kalman filtering algorithm, owing to its optimality in some sense, is widely used in systems and control, signal processing and many other fields. This paper presents a detailed analysis for the L p ‐stability of tracking errors when the Kalman filter is used for tracking unknown time‐varying parameters. The results of this paper differ from the previous ones in that the regression vector (in a linear regression model) or the output matrix (in state space terminology) is random rather than deterministic. The context is kept general so that, in particular, the time‐varying parameter is allowed to be unbounded, and no assumption of stationarity or independence for signals is made.