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Multi-function radars (MFRs) are sophisticated types of sensors with thecapabilities of complex agile inter-pulse modulation implementation and dynamicwork mode scheduling. The developments in MFRs pose great challenges to modernelectronic reconnaissance systems or radar warning receivers for recognitionand inference of MFR work modes. To address this issue, this paper proposes anonline processing framework for parameter estimation and change point detectionof MFR work modes. At first, this paper designed a fully-conjugate Bayesiannon-parametric hidden Markov model with a designed prior distribution (agileBNP-HMM) to represent the MFR pulse agility characteristics. The proposed modelallows fully-variational Bayesian inference. Then, the proposed framework isconstructed by two main parts. The first part is the agile BNP-HMM model forautomatically inferring the number of HMM hidden states and emissiondistribution of the corresponding hidden states. An estimation error lowerbound on performance is derived and the proposed algorithm is shown to be closeto the bound. The second part utilizes the streaming Bayesian updating tofacilitate computation, and designed an online work mode change detectionframework based upon a weighted sequential probability ratio test. Wedemonstrate that the proposed framework is consistently highly effective androbust to baseline methods on diverse simulated data-sets.

Bayesian Non-parametric Hidden Markov Model for Agile Radar Pulse Sequences Streaming Analysis