Abstract: In order to improve the tracking accuracy of the moving object, a moving object tracking algorithm based on the minimum Sigma point slope particle reconstruction filter is proposed. Firstly, we use the split and merge tracker to solve the complex tracking problem under uncertain environment target tracking, the multi direction can be different from the position of the target parallel tracking, which could reduce the loss probability; Secondly, in order to reduce the computational burden due to the unscented particle filter Sigma point aggregation degree, we use the minimum Sigma point slope to improve the trace particle filter algorithm for the fission of sub trackers, which could improve the computational efficiency of the trace particle filter algorithm. Finally, the experimental results show that compared to the several selected location methods, the proposed tracking method can obtain better positioning effect at lower cost, and can balance the network load and improve the detectability of the lifetime of the sensor network.

Key words:

Sigma point, minimum slope, non trace particle filter, particle reconstruction, mobile tracking

摘要： 为提高移动物体的追踪精度，提出一种基于最小Sigma点斜率粒子重构滤波的移动物体追踪算法。首先，利用分裂重构跟踪器解决复杂跟踪环境下目标跟踪的不确定问题，分裂重构形成的多跟踪器，在降低计算复杂度情况下，可从不同位置进行目标多方向并行跟踪，降低目标丢失概率；其次，针对无迹粒子滤波(UPF)Sigma点聚集度差，计算负担大的问题，对裂变获得的子跟踪器，利用最小Sigma点斜率对其粒子滤波过程进行改进，提高无迹粒子滤波算法计算效率；最后，实验结果表明，所提跟踪方法与选定几种定位方法相比，能以更低代价获得更优定位效果，并可均衡网络负载，提高探测传感器网络的使用寿命。

关键词: Sigma点, 最小斜率, 无迹粒子滤波, 粒子重构, 移动追踪