Haiqi Liu

Xuqi Zhang, Fanqin Meng, Haiqi Liu et al.

This paper considers the problem of tracking a large-scale number of group targets. Usually, multi-target in most tracking scenarios are assumed to have independent motion and are well-separated. However, for group target tracking (GTT), the targets within groups are closely spaced and move in a coordinated manner, the groups can split or merge, and the numbers of targets in groups may be large, which lead to more challenging data association, filtering and computation problems. Within the belief propagation (BP) framework, we propose a scalable group target belief propagation (GTBP) method by jointly inferring target existence variables, group structure, data association and target states. The method can efficiently calculate the approximations of the marginal posterior distributions of these variables by performing belief propagation on the devised factor graph. As a consequence, GTBP is capable of capturing the changes in group structure, e.g., group splitting and merging. Furthermore, we model the evolution of targets as the co-action of the group or single-target motions specified by the possible group structures and corresponding probabilities. This flexible modeling enables seamless and simultaneous tracking of multiple group targets and ungrouped targets. Particularly, GTBP has excellent scalability and low computational complexity. It not only maintains the same scalability as BP, i.e., scaling linearly in the number of sensor measurements and quadratically in the number of targets, but also only scales linearly in the number of preserved group partitions. Finally, numerical experiments are presented to demonstrate the effectiveness and scalability of the proposed GTBP method.

Qiaojia Zhu, Xiaojing Shen, Haiqi Liu et al.

Cooperative and non-cooperative localization frequently arise together in wireless sensor networks, particularly when sensor positions are uncertain and targets are unable to communicate with the network. While joint processing can eliminate the delay in target estimation found in sequential approaches, it introduces complex variable coupling, posing challenges in both modeling and optimization. This paper presents a joint modeling approach that formulates cooperative and non-cooperative localization as a single optimization problem. To address the resulting coupling, we introduce auxiliary variables that enable structural decoupling and distributed computation. Building on this formulation, we develop the Scaled Proximal Alternating Direction Method of Multipliers for Joint Cooperative and Non-Cooperative Localization (SP-ADMM-JCNL). Leveraging the problem's structured design, we provide theoretical guarantees that the algorithm generates a sequence converging globally to the Karush-Kuhn-Tucker (KKT) point of the reformulated problem and further to a critical point of the original non-convex objective function, with a sublinear rate of O(1/T). Experiments on both synthetic and benchmark datasets demonstrate that SP-ADMM-JCNL achieves accurate and reliable localization performance.