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

TL;DR

This paper introduces an online Bayesian non-parametric hidden Markov model for real-time analysis of agile radar pulse sequences, enabling automatic inference of work modes and change detection with high robustness.

Contribution

It develops a fully conjugate Bayesian non-parametric HMM with variational inference for agile radar pulse analysis, including an online change detection framework.

Findings

Model closely approaches the theoretical performance bound.

Framework demonstrates high effectiveness and robustness in simulated data.

Enables automatic inference of hidden states and work mode changes.

Abstract

Multi-function radars (MFRs) 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 paper proposes an online processing framework for parameter estimation and change point detection of MFR work modes. At first, this paper 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. The proposed model allows fully-variational Bayesian inference. 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 estimation error lower bound on performance is derived and the proposed algorithm is shown to be close to the bound. The second part utilizes the streaming Bayesian updating to facilitate computation, and designed an online work mode change detection framework based upon a weighted sequential probability ratio test. We demonstrate that the proposed framework is consistently highly effective and robust to baseline methods on diverse simulated data-sets.