Bayesian Nonparametric Hidden Markov Model for Agile Radar Pulse Sequences Streaming Analysis

Multi-function radars are sophisticated types of sensors with the capabilities of complex agile inter-pulse modulation implementation and dynamic work mode scheduling. The developments in MFRs pose great challenges to modern electronic reconnaissance systems or radar warning receivers for recognition and inference of MFR work modes. To address this issue, this article proposes an online processing framework for parameter estimation and change point detection of MFR work modes. At first, this article designed a fully-conjugate Bayesian Non-Parametric Hidden Markov Model with a designed prior distribution (agile BNP-HMM) to represent the MFR pulse agility characteristics. Then, the proposed framework is constructed by two main parts. The first part is the agile BNP-HMM model for automatically inferring the number of HMM hidden states and emission distribution of the corresponding hidden states. An error lower bound is derived for estimation performance and the proposed algorithm is shown to be closer to the bound compared with baseline methods. The second part combines the streaming Bayesian updating to facilitate computation, and designed an online work mode change detection framework based upon the weighted sequential probability ratio test. We demonstrate that the proposed framework is consistently highly effective and robust to baseline methods on diverse simulated radar signal data and real-life benchmark datasets. The source code is available at https://github.com/JiadiBao/Agile-BNP-HMM.