Si Weijian, Wang Liwei, Qu Zhiyu
College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China.
Sensors (Basel). 2016 Jun 17;16(6):901. doi: 10.3390/s16060901.
The sequential Monte Carlo probability hypothesis density (SMC-PHD) filter has been demonstrated to be a favorable method for multi-target tracking. However, the time-varying target states need to be extracted from the particle approximation of the posterior PHD, which is difficult to implement due to the unknown relations between the large amount of particles and the PHD peaks representing potential target locations. To address this problem, a novel multi-target state extraction algorithm is proposed in this paper. By exploiting the information of measurements and particle likelihoods in the filtering stage, we propose a validation mechanism which aims at selecting effective measurements and particles corresponding to detected targets. Subsequently, the state estimates of the detected and undetected targets are performed separately: the former are obtained from the particle clusters directed by effective measurements, while the latter are obtained from the particles corresponding to undetected targets via clustering method. Simulation results demonstrate that the proposed method yields better estimation accuracy and reliability compared to existing methods.
序贯蒙特卡罗概率假设密度（SMC-PHD）滤波器已被证明是一种用于多目标跟踪的有效方法。然而，时变目标状态需要从后验PHD的粒子近似中提取，由于大量粒子与代表潜在目标位置的PHD峰值之间的未知关系，这很难实现。为了解决这个问题，本文提出了一种新颖的多目标状态提取算法。通过利用滤波阶段的测量信息和粒子似然性，我们提出了一种验证机制，旨在选择与检测到的目标相对应的有效测量和粒子。随后，分别对检测到的和未检测到的目标进行状态估计：前者从有效测量引导的粒子簇中获得，而后者通过聚类方法从与未检测到的目标相对应的粒子中获得。仿真结果表明，与现有方法相比，该方法具有更高的估计精度和可靠性。
