基于DSmT与粒子滤波的多传感器融合

为实现多传感器对机动目标状态的跟踪，提出一种基于DSmT与粒子滤波的多传感器融合算法。在各传感器利用粒子滤波方法处理观测数据的基础上，运用DSmT作为融合工具，将观测数据转化为辨识框架内的元素及其mass值，得到最终融合结果。实验结果表明，该方法可减小距离误差，提高跟踪精度，且运算复杂度能满足在线实时融合的要求。

Multi-sensor Fusion Based on DSmT and Particle Filtering

To realize multi-sensor tracking for maneuvering target state, this paper presents a multi-sensor fusion algorithm based on DSmT and particle filtering. On the basis of observation date which is delivered by multi-sensor and filtered by particle filters. It chooses DSmT as the combining tool. The observation data is transformed into the elements and masses of the frame of discernment, and gets the finally result. Experimental results show that this algorithm can reduce distance error and improve tracking accuracy, and the appropriate computational complexity can satisfy the demand of fusing on-line.