Multiple extended target tracking (ETT) has gained increasing attention due to the development of high-precision LiDAR and radar sensors in automotive applications. For LiDAR point cloud-based vehicle tracking, this paper presents a probabilistic measurement-region association (PMRA) ETT model, which can describe the complex measurement distribution by partitioning the target extent into different regions. The PMRA model overcomes the drawbacks of previous data-region association (DRA) models by eliminating the approximation error of constrained estimation and using continuous integrals to more reliably calculate the association probabilities. Furthermore, the PMRA model is integrated with the Poisson multi-Bernoulli mixture (PMBM) filter for tracking multiple vehicles. Simulation results illustrate the superior estimation accuracy of the proposed PMRA-PMBM filter in terms of both the positions and extents of vehicles compared with PMBM filters using the gamma Gaussian inverse Wishart and DRA implementations.

This paper presents a new trajectory Poisson multi-Bernoulli (TPMB) filter for group target tracking. Due to the collective behavior and dense spatial distribution, exact trajectory estimation is an extremely challenging task in group target tracking. Aiming for improved performance in group trajectory estimation, the virtual leader-follower model is incorporated into the standard TPMB filter in this paper to address the coordinated motion within groups. Moreover, the Gaussian implementation and $\boldsymbol{L}$-scan approximation for the proposed group target trajectory PMB (GTTPMB) filter are also provided. Finally, a simulation scenario with splitting and merging of groups is established to evaluate the proposed GTTPMB filter. The results demonstrate that the proposed filter can effectively estimate the trajectories of group members without introducing additional computational burden.

The multiple hypothesis tracker (MHT) is a popular algorithm for solving multi-target tracking (MTT) problem in cluttered environment. It is known as a maximum a posterior (MAP) estimator which enumerates all possible global hypotheses and dedicates to find the most likely solution based on the received reports. However, its practical application is often limited by the complexity of data association step. This paper describes an efficient MHT data association algorithm which based on the “track-oriented” MHT framework. The proposed approach translates the data association problem to the maximum weight independent set problem (MWISP) and introduces a graph representation to describe the track hypotheses and the compatibility restrictions between them. In this way, the MAP assignment in tracking application can be solved by applying max-product belief propagation (MPBP) inference algorithm to the corresponding graph. Empirical results demonstrate that the MPBP-MHT algorithm outperforms other algorithms in tracking performance even in challenging closely-spaced MTT case.

In order to improve the comprehensive defense capability of netted radar system while tracking the multi-targets cooperatively, a more reasonable evaluation indicator is required to evaluate the overall performance of the netted radar system. The new evaluation indicators for radar deployment and allocation in the netted radar management are proposed quantitatively in this paper. As they consist of coverage ratio, tracking accuracy and anti-jamming capability, the new evaluation indicators can ensure the netted radar system possesses an outstanding anti-jamming capability through radar management. Simulations show that the arrangement result is more reasonable when the new evaluation indicators are applied relative to the convectional indicators. And the optimization process based on the new indicators can make the netted radar system more suitable to the complex situations in practice.