Exploring on Hierarchical KalmanFiltering Fusion Algorithm

Aim To analyze the traditional hierarchical Kalman filtering fusion algor- ithm theoretically. explain that the traditional Kalman filtering fusion algorithm is complex and can not improve the tracking precision well , and propose the weighting average fusion algorithm. Methods The theoretical analysis and Monte Carlo simulation methods were used to compare the traditional fusion algorithm with the new algorithm. and the statistical values of the root- mean- square error of the two algorithms were computed. Results The weighting filtering fusion algorithm is simple in principle, less in data, faster in processing and better in tolerance. Conclusion 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.