概率强近邻在IMM中的应用

Interacting Multi-Model Algorithm Based on Probabilistic Data Association Filter

目标的幅度信息在数据关联技术中起着很重要的作用,但传统的方法忽视了对这类信息的应用。提出了一种基于概率强近邻的相互作用多模型算法,有效地将幅度信息引入到数据关联中,完成对目标的精确跟踪。通过仿真,将它与传统的相互作用多模型概率数据关联等算法进行比较。仿真结果表明,该算法不仅具有很好的跟踪精度,而且其计算量也大大降低。

Amplitude information of target is very important for data association. However, traditional methods always ignore the application of this kind of information. A new approach named Interacting Multiple Models （IMM） algorithm was put forward based on Probabilistic Data Association Filter to introduce amplitude information into data association for tracking the target precisely. Simulations were conducted to compare it with traditional methods. The results proved that the algorithm present here has high tracking precision and greatly reduces the calculation cost.