Application of Adaptive Unscented Kalman Filter on MEMS Integrated Navigation System
The efficient and accurate approximate nonlinear filters have been widely used in estimation of the states and parameters of dynamical systems. In this paper we have designed an adaptive unscented Kalman filter for precise estimation of states and parameters of MEMS. First, as sampling-based nonlinear filters, the unscented Kalman filter was investigated. Secondly, the adaptive unscented Kalman filter (AUKF) was designed. The adaptive nonlinear filter have take into account the incorrect time-varying noise statistics of dynamical systems, as well as to compensate the nonlinearity effects neglected by linearization. In the end, Simulation results were given. Simulation results indicate that the advantages of the proposed nonlinear filters make these attractive alternatives to the extended Kalman filter.
CHEN Xue-jiang LI Shi-xin YU Neng-peng YANG Ye
The Department of Airborne Equipment, Army Aviation Institute, Beijing 101123, China Tianjin Navigation Instruments Research Institute, Tianjin 300131, China
国际会议
第八届国际测试技术研讨会(8th International Symposium on Test and Measurement)
重庆
英文
1793-1796
2009-08-01（万方平台首次上网日期，不代表论文的发表时间）