非线性系统中多传感器滤波跟踪型数据融合算法的研究

在非线性系统中，常用的跟踪滤波算法是基于扩展的卡尔曼滤波算法的融合算法，但是这种融合算法的跟踪精度并不是很高。本文根据对滤波器跟踪型数据融合的研究，提出了基于转换测量值卡尔曼滤波算法的非线性系统中的数据融合方法。研究表明，在利用激光干涉仪进行目标跟踪时，这种基于融合算法的集中式融合算法的跟踪性能优于分布式融合算法，但是，从仿真结果可以看出，两种融合算法的差别不大，结果基本相同，因此，在非线性系统中，基于转换测量值卡尔曼滤波算法的分布融合算法可以重构集中式融合算法。

Research on Fusion Algorithm for Multi-sensor Filtering Target Tracking in Nonlinear Systems

In nonlinear systems, the fusion algorithm based on extended Kalman Filter suffers from the disadvantage that the tracking precision is not satisfied. In this paper, a fusion algorithm in nonlinear systems based on converted measurement Kalman filter is put forward. The result of simulation shows that the result of centralized converted measurement Kalman filtering is better than the result of converted measurement Kalman filtering. But we can see that the difference between these two algorithms is small. So it can be concluded that in nonlinear systems distributed fusion algorithm based on converted measurement Kalman filtering can basically reconstruct centralized fusion algorithm.