Multiple-Model GLMB Filter Based on Track-Before-Detect for Tracking Multiple Maneuvering Targets

A generalized labeled multi-Bernoulli (GLMB) filter with motion mode label based on the track-before-detect (TBD) strategy for maneuvering targets in sea clutter with heavy tail, in which the transitions of the mode of target motions are modeled by using jump Markovian system (JMS), is presented in this paper. The close-form solution is derived for sequential Monte Carlo implementation of the GLMB filter based on the TBO model. In update, we derive a tractable GLMB density, which preserves the cardinality distribution and first-order moment of the labeled multi-target distribution of interest as well as minimizes the Kullback-Leibler divergence (KLD), to enable the next recursive cycle. The relevant simulation results prove that the proposed multiple-model GLMB-TBD (MM-GLMB-TBD) algorithm based on K-distributed clutter model can improve the detecting and tracking performance in both estimation error and robustness compared with state-of-the-art algorithms for sea clutter background. Additionally, the simulations show that the proposed MM-GLMB-TBD algorithm can accurately output the multitarget trajectories with considerably less computational complexity compared with the adapted dynamic programming based TBD (DP-TBD) algorithm. Meanwhile, the simulation results also indicate that the proposed MM-GLMB-TBD filter slightly outperforms the JMS particle filter based TBD (JMS-MeMBer-TBD) filter in estimation error with the basically same computational cost. Finally, the impact of the mismatches on the clutter model and clutter parameter is investigated for the performance of the MM-GLMB-TBD filter.