多目标跟踪的概率假设密度粒子滤波

在多目标跟踪中,当目标数很大时,目标状态的联合分布的计算量会非常大。如果目标独立运动,可用各目标分别滤波来代替,但这要求考虑数据互联问题。文章介绍一种可以解决计算量问题的方法,只需计算联合分布的一阶矩——概率假设密度(PHD),PHD在任意区域S上的积分是S内目标数的期望值。因未记录目标身份,避免了数据互联问题。仿真中,传感器为被动雷达,目标观测值为距离、角度及速度时,对上述的PHD滤波进行了粒子实现,并对观测值是否相关的不同情况进行比较。PHD粒子滤波应用在非线性模型的多目标跟踪,实验结果表明,滤波可以稳健跟踪目标数为变数的情况,得到了接近真实情况的结果。

Multi-target Tracking with Probability Hypothesis Density Particle Filter

When tracking a large number of targets, it is often computationally expensive to represent the full joint distribution over target states. In cases where the targets move independently, each target can be tracked with a separate filter, however, this leads to a model-data association problem. An approach is introduced to solve the problem with computational complexity is to track only the first moment of the joint distribution, the probability hypothesis density (PHD), the integral of this distribution over any area S is the expected number of targets within S. Since no record of object identity is kept, the model-data association problem is avoided. This PHD particle filter is applied to tracking of multiple targets, a non-linear tracking problem in which the sensor is passive radar with range, bearing and Doppler velocity observations. Compared the results of independent observations with results of correlate observations, experiments show that the filter can track a changing number of targets robustly, achieving near-real-time performance.