标签箱粒子概率假设密度群目标跟踪算法

针对现有的箱粒子概率假设密度(probability hypothesis density,PHD)群目标跟踪算法计算量大、在群数目较多时状态提取不稳定以及无法获得群的航迹等问题,提出标签箱粒子PHD群目标跟踪算法。该算法首先对量测进行预处理,剔除其中的杂波量测,以降低量测更新的计算量。然后,通过为箱粒子添加标签,区分不同的群目标,获得不同群的航迹。最后,依据不同标签提取群目标的状态,有效避免k-means聚类不稳定带来的影响。仿真实验表明,所提算法具有运算量小,在漏检环境下仍能很好地维持不同群的航迹,并在群数目较多时可准确提取群目标状态等优点。

Group target tracking algorithm based on labeled box particle probability hypothesis density

Aiming at the problems of the existing box particle probability hypothesis density (PHD) based group target tracking algorithm such as a heavy computational burden, poor stability of the extracting state with a large number of clusters and unavailability of group trajectories, a labeled box particle PHD group tracking algorithm is proposed. First, the measurements are preprocessed to eliminate the clutter measurements, so as to reduce the computational burden of the measurement updating step. Then, by adding labels to the box particles, different group targets are distinguished, and the trajectories of different group targets can be obtained. Finally, the states of group targets are extracted according to different labels and the impact of k-means clustering instability are effectively avoided. Simulation experiments illustrate the advantages of the proposed algorithm in terms of light computational burden, track maintenance of different groups under the environment of miss detection, and accurate extraction of the group target state in the case of a large number of clusters.