Zhu Youqing, Zhou Shilin, Gao Gui, Zou Huanxin, Lei Lin
College of Electronic Science and Engineering, National University of Defense Technology, Changsha, Hunan, China.
PLoS One. 2014 Dec 9;9(12):e114317. doi: 10.1371/journal.pone.0114317. eCollection 2014.
If equipped with several radar emitters, a target will produce more than one measurement per time step and is denoted as an extended target. However, due to the requirement of all possible measurement set partitions, the exact probability hypothesis density filter for extended target tracking is computationally intractable. To reduce the computational burden, a fast partitioning algorithm based on hierarchy clustering is proposed in this paper. It combines the two most similar cells to obtain new partitions step by step. The pseudo-likelihoods in the Gaussian-mixture probability hypothesis density filter can then be computed iteratively. Furthermore, considering the additional measurement information from the emitter target, the signal feature is also used in partitioning the measurement set to improve the tracking performance. The simulation results show that the proposed method can perform better with lower computational complexity in scenarios with different clutter densities.
如果一个目标配备了多个雷达发射器，那么它在每个时间步会产生多个测量值，这种目标被称为扩展目标。然而，由于对所有可能的测量集划分有要求，用于扩展目标跟踪的精确概率假设密度滤波器在计算上是难以处理的。为了减轻计算负担，本文提出了一种基于层次聚类的快速划分算法。它通过逐步合并两个最相似的单元来获得新的划分。然后可以迭代地计算高斯混合概率假设密度滤波器中的伪似然值。此外，考虑到来自发射器目标的额外测量信息，信号特征也被用于划分测量集以提高跟踪性能。仿真结果表明，在不同杂波密度的场景中，所提出的方法能够以较低的计算复杂度实现更好的性能。
