Modeling and Interpretation of Multifunction Radars with Stochastic Grammar

TitleModeling and Interpretation of Multifunction Radars with Stochastic Grammar
Publication TypeConference Paper
Year of Publication2008
AuthorsWang, A., and V. Krishnamurthy
Conference NameAerospace Conference, 2008 IEEE
Pagination1 -13
Date Publishedmar.
Keywordsaircraft survivability, complex dynamical modes, domain expert knowledge, electronic warfare, knowledge-based statistical signal processing technique, Markov chain, Markov modulated stochastic context free grammar, Markov processes, maximum likelihood sequence estimation, maximum likelihood sequence estimator, military radar, multifunction radars, radar signal, signal processing, statistical signal interpretation, stochastic grammar, syntactic representation

Multifunction radars (MFRs) are sophisticated sensors with complex dynamical modes that are widely used in surveillance and tracking. Because of their agility, a new solution to the interpretation of radar signal is critical to aircraft survivability and successful mission completion. In this paper, we introduce a knowledge-based statistical signal processing technique that allows syntactic representation of domain expert knowledge. In particular, we model MFRs as systems that "speak" a language that can be characterized by a Markov modulated stochastic context free grammar (SCFG). We demonstrate that SCFG, modulated by a Markov chain, serves as an adequate knowledge representation of MFRs' dynamics. We then deal with the statistical signal interpretation, the threat evaluation, of the MFR signal. Two statistical estimation algorithms for MFR signal are derived - a maximum likelihood sequence estimator to estimate the system state, and a maximum likelihood parameter estimator to infer the system parameter values. Based on the interpreted radar signal, the interaction dynamics between the MFR and the target is studied and the control of the aircraft's maneuvering models is implemented.


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