Multiple sensor Multi-Bernoulli filter based track-before-detect for polarimetric MIMO radars

In this paper, we deal with the problem of simultaneously detecting and tracking multiple targets using polarimetric multiple input multiple output (MIMO) radars. The problem is formulated in a Bayesian framework by modeling the collection of states as a random finite set. First, we propose a multiple sensor Multi-Bernoulli (MS-MeMber) filter based track-before-detect (TBD) algorithm suitable for both MIMO radars and polarimetric MIMO radars. Then the sequential Monte Carlo (SMC) implementations are performed to prove the effectiveness of the proposed algorithm. Simulation results show that the polarization diversity can be exploited to enhance the detecting and tracking performance of MIMO radars.