School of Air Defense and Missile Defense, Air Force Engineering University, Xi'an 710051, China.
Graduate School, Air Force Engineering University, Xi'an 710051, China.
Sensors (Basel). 2021 Dec 23;22(1):70. doi: 10.3390/s22010070.
Gaussian mixture probability hypothesis density (GM-PHD) filtering based on random finite set (RFS) is an effective method to deal with multi-target tracking (MTT). However, the traditional GM-PHD filter cannot form a continuous track in the tracking process, and it is easy to produce a large number of redundant invalid likelihood functions in a dense clutter environment, which reduces the computational efficiency and affects the update result of target probability hypothesis density, resulting in excessive tracking error. Therefore, based on the GM-PHD filter framework, the target state space is extended to a higher dimension. By adding a label set, each Gaussian component is assigned a label, and the label is merged in the pruning and merging step to increase the merging threshold to reduce the Gaussian component generated by dense clutter update, which reduces the computation in the next prediction and update. After pruning and merging, the Gaussian components are further clustered and optimized by threshold separation clustering, thus as to improve the tracking performance of the filter and finally realizing the accurate formation of multi-target tracks in a dense clutter environment. Simulation results show that the proposed algorithm can form a continuous and reliable track in dense clutter environment and has good tracking performance and computational efficiency.
基于随机有限集（RFS）的高斯混合概率假设密度（GM-PHD）滤波是处理多目标跟踪（MTT）的一种有效方法。然而，传统的 GM-PHD 滤波器在跟踪过程中无法形成连续的轨迹，并且在密集杂波环境中容易产生大量冗余的无效似然函数，这降低了计算效率并影响目标概率假设密度的更新结果，导致跟踪误差过大。因此，在 GM-PHD 滤波器框架的基础上，将目标状态空间扩展到更高维度。通过添加标签集，为每个高斯分量分配一个标签，并在修剪和合并步骤中合并标签，以增加合并阈值，减少密集杂波更新生成的高斯分量，从而减少下一次预测和更新中的计算量。修剪和合并后，通过阈值分离聚类对高斯分量进行进一步聚类和优化，从而提高滤波器的跟踪性能，最终实现密集杂波环境中多目标轨迹的准确形成。仿真结果表明，所提出的算法能够在密集杂波环境中形成连续可靠的轨迹，具有良好的跟踪性能和计算效率。
