Exploring on hierarchical Kalman filtering fusion accuracy

This paper analyzes the traditional hierarchical Kalman filtering fusion algorithm theoretically and points out that the traditional kalman filtering fusion algorithm is complex and can not improve the tracking precision well, even it is impractical. The theoretical analysis and Monte Carlo simulation methods were used to compare the traditional fusion algorithm with the new one, and the comparison of the root mean square error statistics values of the two algorithms was made. The hierarchical fusion algorithm is not better than the weighting average fusion and feedback weighting average algorithm. The weighting filtering fusion algorithm is more simple in principle, less in data, faster in processing and better in tolerance. The weighting hierarchical fusion algorithm is suitable for the defective sensors. The feedback of the fusion result to the single sensor can enhance the single sensor's precision, especially once one sensor has great deviation and low accuracy or has some deviation of sample period and is asynchronous to other sensors.