非线性系统中多传感器目标跟踪融合算法研究

 研究了在非线性系统中 ,基于转换坐标卡尔曼滤波器的多传感器目标跟踪融合算法。通过分析得出 :在非线性系统的多传感器目标跟踪中 ,基于转换坐标卡尔曼滤波器 ( CMKF)的分布融合估计基本可以重构中心融合估计。仿真实验也证明了此结论。由此可见分布的 CMKFA是非线性系统中较优的分布融合算法

RESEARCH ON FUSION ALGORITHM FOR MULTI-SENSOR TARGET TRACKING IN NONLINEAR SYSTEMS

There are three basic fusion algorithms for target-tracking, which are centralized fusion algorithm, distributed fusion algorithm and hybrid fusion algorithm. Centralized fusion algorithm can achieve the highest tracking accuracy but it needs heavy processing load in fusion center and higher communication load. Recently, the distributed algorithm has received significant attention in multi-sensor target tracking for its light processing load in fusion center and lower communication load. In linear systems the distributed fusion can succeed to reconstruct the optimal centralized fusion estimate by combining the local estimates. In nonlinear systems, converted measurement Kalman filtering algorithm (CMKFA) is better than extended Kalman filtering algorithm (EKFA) for target tracking. This paper mainly studies data fusion algorithm based on converted measurement Kalman filter (CMKF) for target tracking in nonlinear systems. From theoretical analysis, it is derived that the distributed converted measurement Kalman filtering algorithm (DCMKFA) can basically reconstruct centralized fusion estimate. And simulation results can prove this conclusion. So DCMKFA is a better distributed fusion algorithm in nonlinear systems.