基于高斯粒子JPDA滤波的多目标跟踪算法

 在多目标跟踪中，由于观测的不确定性带来数据关联问题，并且，多目标状态空间尺寸的增长带来了维数增大问题，该文提出了一种新的高斯粒子联合概率数据关联滤波算法(GP-JPDAF)，在JPDA框架中引入高斯粒子滤波(GPF)的思想，通过高斯粒子而不是高斯量，来近似目标与观测的边缘关联概率，利用GPF计算目标状态的预测及更新分布。将其应用于被动多传感器多目标跟踪，仿真结果表明该算法比MC-JPDAF具有更好的跟踪性能。

Gaussian Particle JPDA Filter Based Multi-target Tracking

In multi-target tracking, aiming at the data association problem that arises due to indistinguishable measurements in the presence of clutter, and the curse of dimensionality that arises due to the increased size of the state-space associated with multiple targets, a novel algorithm based on Gaussian Particle Joint Probabilistic Data Association Filter (GP-JPDAF) is proposed, which introduces Gaussian Particle Filtering (GPF) concept to the JPDA framework. For each of the targets, the marginal association probabilities are approximated with Gaussian particles rather than Gaussians in the JPDAF. Moreover, GPF is utilized for approximating the prediction and update distributions. Finally, the proposed method is applied to passive multi-sensor multi-target tracking. Simulation results show that the method can obtain better tracking performance than Monte Carlo JPDAF (MC -JPDAF).