Performance Comparisons of Adaptive Cubature Kalman Filters
The performances of three classical adaptive cubature Kalman filters (ACKFs) for nonlinear stochastic discrete-time system with unknown process noise are investigated.First,filtering theories of the ACKFs are discussed,and then stability analysis and accuracy comparison simulations are performed.It is proved that cubature Hinfinity filter(CH∞F) can provide an extra positive-definite matrix to improve its stability and other two ACKFs both need a good choice of process noise initial covariance for better stability.Simulation results also show that all the three ACKFs can have better accuracy than the traditional CKF with unknown process noise.Furthermore,CH∞F can most quickly follow the process noise change in high dimension case but it do the worst of three ACKFs in one dimension filtering; cubature Kalman filter-strong tracking filtering(CKF-STF) can keep good accuracy in all examples; adaptive cubature Kalman filter based on maximum a posterior estimation(ACKF-MAP) is prone to deterioration in high dimensions filtering,whereas it has good performance in one dimension cases.
Kalman filters nonlinear system adaptive filtering maneuvering target tracking stability
Sisi Wang Guoqing Qi Quanbo Ge
School of information science and technology, Dalian Maritime University, DMU Dalian, China School of Automation Hangzhou Dianzi University, GDOU Zhanjiang, China
国际会议
秦皇岛
英文
209-214
2015-09-18（万方平台首次上网日期，不代表论文的发表时间）