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.
Modeling and Interpretation of Multifunction Radars with Stochastic Grammar
01.03.2008
4301931 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Track formation range in multifunction array radars
British Library Conference Proceedings | 1997
|Pulse Deinterleaving for Multifunction Radars With Hierarchical Deep Neural Networks
IEEE | 2021
|Recognition of Multifunction Radars Via Hierarchically Mining and Exploiting Pulse Group Patterns
IEEE | 2020
|