Multiple fluctuating targets track-before-detect using multi-Bernoulli filter in radar sensor

Abstract

This paper addresses the detection and tracking of multiple fluctuating targets for a track-before-detect algorithm based on the Multi-Bernoulli (MB-TBD) filter in surveillance radar systems. MB-TBD usually considers target amplitude information and ignores the fact that radar measurements are complex-valued. In this paper, we first propose to utilize phase information to improve the discrimination of targets from noise. More precisely, complex likelihood ratios are used instead of squared modulus measurements likelihood ratios for fluctuations of types Swerling 0, 1, 3. Secondly, the traditional MB-TBD filter cannot solve the problem of coexistence between targets with stronger amplitude and weaker amplitude when multiple fluctuating targets are moving. To address this limitation, an adaptive birth distribution based on joint successive target cancellation and measurement likelihood ratio driven is proposed. Moreover, in order to reduce computational complexity, the Bernoulli components of the same targets are merged after the MB-TBD updating. Finally, the proposed algorithm is implemented using Sequential Monte Carlo technology. The simulation results show that in challenging scenarios, the performance of the improved algorithm is better than the traditional algorithm, and it has a good application prospect.