A group target track-before-detect approach using two-stage strategy with maximum-likelihood probabilistic data association

In tracking scenarios involving groups with dense targets, achieving effective data association is challenging due to mutual occlusion and interference among targets. The complexity of the tracking problem is further exacerbated in low-observable environments by the increase in false alarm rates. The track-before-detect (TBD) is an advanced technology for detecting and tracking low-observable targets, effectively mitigating data association problems by integrating multi-frame echo data. However, the existing multi-target TBD algorithms typically assume that the targets are spatially separated and are not suitable for scenarios involving group targets. A group target maximum-likelihood probabilistic data association (GT-ML-PDA) algorithm, based on the concept of TBD, is proposed to track group targets effectively in low-observable environments. The proposed algorithm divides group target tracking into two stages: group centre trajectory estimation and individual target trajectory estimation. To enhance the performance of the proposed algorithm, two strategies are suggested: modifying the equivalent measurements and extracting independent measurement sets for individual targets. Simulation results demonstrate that the proposed algorithm is capable of effectively tracking numerous individual targets within a group, even in the presence of heavy clutter.