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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.