Generalized pseudo-Bayes estimation and detection for abruptly changing systems

Watanabe, K; Tzafestas, SG

dc.contributor.author	Watanabe, K	en
dc.contributor.author	Tzafestas, SG	en
dc.date.accessioned	2014-03-01T01:09:25Z
dc.date.available	2014-03-01T01:09:25Z
dc.date.issued	1993	en
dc.identifier.issn	0921-0296	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/10968
dc.subject	Bayes methods	en
dc.subject	failure detection	en
dc.subject	Kalman filters	en
dc.subject	Markov processes	en
dc.subject	nonlinear filtering	en
dc.subject	stochastic systems	en
dc.subject.classification	Computer Science, Artificial Intelligence	en
dc.subject.classification	Robotics	en
dc.subject.other	DISCRETE-SYSTEMS	en
dc.subject.other	STATE ESTIMATION	en
dc.subject.other	MANEUVERING TARGETS	en
dc.subject.other	JUMP PARAMETERS	en
dc.subject.other	TRACKING	en
dc.subject.other	IDENTIFICATION	en
dc.title	Generalized pseudo-Bayes estimation and detection for abruptly changing systems	en
heal.type	journalArticle	en
heal.identifier.primary	10.1007/BF01258214	en
heal.identifier.secondary	http://dx.doi.org/10.1007/BF01258214	en
heal.language	English	en
heal.publicationDate	1993	en
heal.abstract	The problem of state estimation and system-structure detection for linear discrete-time systems with unknown parameters which may switch among a finite set of values is considered. The switching parameters are modeled by a Markov chain with known transition probabilities. Since the optimal solutions require exponentially growing storage and computations with time, a new method of generalized pseudo-Bayes algorithm (GPBA) is proposed to circumvent this problem by using a multi-stage measurement update technique. A minor modification is also presented to correct a defect of the Jaffer and Gupta method. Some simulation comparisons are included to illustrate the effectiveness of the proposed algorithms. It is then shown that, as compared with other GPBAs, a feature of the present GPBA is that it noticeably decreases the size of the required memory when the number of states in the Markov chain is large. The cost to be paid is a slight increase in the computing time. © 1993 Kluwer Academic Publishers.	en
heal.publisher	Kluwer Academic Publishers	en
heal.journalName	Journal of Intelligent & Robotic Systems	en
dc.identifier.doi	10.1007/BF01258214	en
dc.identifier.isi	ISI:A1993KJ27100006	en
dc.identifier.volume	7	en
dc.identifier.issue	1	en
dc.identifier.spage	95	en
dc.identifier.epage	112	en