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中文摘要：
基于有限集统计理论的概率假设密度滤波算法运用于多目标跟踪时,不再考虑数据关联问题,突破了传统的跟踪方法。但该滤波公式在非线性条件下没有解析解,在非线性高斯条件下提出了基于无迹变换的概率假设密度滤波算法,实现了算法在强杂波环境下的多目标跟踪。仿真实验比较了该算法与基于粒子滤波的概率假设密度滤波算法的跟踪性能,验证了该算法的跟踪性能和精度。同时分析指出了此算法的不足,以及结合无迹变换与粒子滤波的概率假设密度滤波算法的改进研究方向。
英文摘要：
The Probability Hypothesis Density （PHD） Filter based on Finite Set Statistics doesn＇t need data association for multi-target tracking, which breaks through the tradition tracking method. But there is no closed form solution to the PHD recursion under the nonlinear models. The Probability Hypothesis Density Filter based on Unscented Kalman filter algorithm was proposed for jointly estimating the time-varying number of targets and their states under clutter environment, Simulation result validated UKF-PHD performance and then compared UKF-PHD and PF-PHD performance. Lastly, the algorithm＇s lacks were pointed out and the direction was researched based on unscented particle filter.
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