Threat Estimation of Multifunction Radars: Modeling and Statistical Signal Processing of Stochastic Context Free Grammars

TitleThreat Estimation of Multifunction Radars: Modeling and Statistical Signal Processing of Stochastic Context Free Grammars
Publication TypeConference Paper
Year of Publication2007
AuthorsWang, A., and V. Krishnamurthy
Conference NameAcoustics, Speech and Signal Processing, 2007. ICASSP 2007. IEEE International Conference on
PaginationIII-793 -III-796
Date Publishedapr.
KeywordsBayes methods, Bayesian estimator, complex dynamical, context-free grammars, Markov chain, Markov processes, maximum likelihood estimation, maximum likelihood estimator, multifunction radars, radar signal processing, radar tracking, statistical signal processing problems, stochastic context free grammar, stochastic context free grammars, threat estimation, tracking systems
Abstract

Multifunction radars (MFRs) are sophisticated sensors with complex dynamical modes that are widely used in surveillance and tracking systems. It is shown in this paper that the stochastic context free grammar (SCFG) is an adequate model for capturing the essential features of the MFR dynamics. We model MFRs as systems that "speak" according to a SCFG, and the grammar is modulated by a Markov chain representing MFRs' policies of operation. We then deal with the statistical signal processing problems of the MFR signal, especially the problem of threat evaluation (electronic support). Maximum likelihood estimator is derived to estimate the threat of the MFR and Bayesian estimator to infer the system parameter values.

URLhttp://dx.doi.org/10.1109/ICASSP.2007.366799
DOI10.1109/ICASSP.2007.366799

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