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.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Modeling and Interpretation of Multifunction Radars with Stochastic Grammar


    Contributors:


    Publication date :

    2008-03-01


    Size :

    4301931 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Track formation range in multifunction array radars

    Sclano, E. / Difesa, A. / International Maritime Defence Exhibition and Conference | British Library Conference Proceedings | 1997





    Syntactic Modeling and Neural-Based Parsing for Multifunction Radar Signal Interpretation

    Feng, Han Cong / Jiang, Kai Li / Zhou, Zhixin et al. | IEEE | 2024