一种基于多传感器的复合量测IMM-EKF数据融合算法

为了提高多传感器系统的目标跟踪精度，且解决传感器数量多导致的耗时长的问题，提出了一种复合量测IMM-EKF（Interacting Multiple Model-Extended Kalman Filter）融合算法.该算法根据各传感器的测量精度，对各传感器关于同一目标的量测点迹进行加权融合，再将融合后的点迹进行IMM-EKF滤波处理.通过仿真及实验数据处理，将复合量测IMM-EKF融合算法与加权IMM-EKF融合算法、扩维IMM-EKF融合算法进行了对比分析，比较了三种算法的跟踪精度及耗时长度.结果表明，扩维IMM-EKF融合算法具有最优的跟踪精度，复合量测IMM-EKF融合算实时性最好.最后分别给出了三种算法的适用场合.

A Composite Measurement IMM-EKF Data Fusion Algorithm Based on Multi-sensor

In order to improve the target tracking accuracy of multi-sensor system as well as solve the problem of long processing time due to the multiple sensors,a composite measurement IMM-EKF(Interacting Multiple Model-Extended Kalman Filter) data fusion algorithm is proposed.According to the measurement accuracy of each sensor,the algorithm weights and fuses the measurement of all sensors with respect to the same target,and performs the IMM-EKF filtering process on the merged measurement.The composite measurement IMM-EKF algorithm is compared with the weighted IMM-EKF algorithm and the extended dimension IMM-EKF algorithm in tracking accuracy and processing time through simulation and experiment data processing.The result shows that the extended dimension IMM-EKF algorithm has the best tracking accuracy while the composite measurement IMM-EKF algorithm needs the shortest processing time.The adapted occasions of the three fusion algorithms are given in the end.