Sequential Detection of Markov Targets With Trajectory Estimation
Grossi, E.
Lops, M.
DAEIMI, Univ. degli Studi di Cassino, Cassino;
This paper appears in: Information Theory, IEEE Transactions on
Publication Date: Sept. 2008
Volume: 54,
Issue: 9
On page(s): 4144-4154
ISSN: 0018-9448
INSPEC Accession Number: 10180425
Digital Object Identifier: 10.1109/TIT.2008.928261
Current Version Published: 2008-08-26
Abstract
The problem of detection and possible estimation of a signal generated by a dynamic system when a variable number of noisy measurements can be taken is here considered. Assuming a Markov evolution of the system (in particular, the pair signal-observation forms a hidden Markov model (HMM)), a sequential procedure is proposed, wherein the detection part is a sequential probability ratio test (SPRT) and the estimation part relies upon a maximum aposteriori probability (MAP) criterion, gated by the detection stage (the parameter to be estimated is the trajectory of the state evolution of the system itself). A thorough analysis of the asymptotic behavior of the test in this new scenario is given, and sufficient conditions for its asymptotic optimality are stated, i.e., for almost sure minimization of the stopping time and for (first-order) minimization of any moment of its distribution. An application to radar surveillance problems is also examined.
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