Application of Adaptive Kalman Filter in Micro-Electro-Mechanical Systems
Sage-Husa adaptive Kalman filtering algorithm is presented for the digital system of MEMS gyroscope. The time series analysis technique is used to set the ARMA model of MEMS gyro random drift based on the analysis of the detection signal in the condition of the additive white Gaussian noise, and the state and observation model. Moreover, the simulation system is built by the Matlab. Sage-Husa adaptive Kalman algorithm has the shorter convergence time in the condition of the unknown statistical characteristics of the noise in the detection signal. In conclusion, the novel Sage-Husa adaptive Kalman filtering algorithm can more real-timely and precisely estimate the state of system than standard kalman filter arithmetics in the detection signal from gyroscope, and increase the SNR of the signals dramatically.
sage-husa kalman filtering MEMS ARMA
Wang Yuhui
School of Science, Qingdao Technological University,Qingdao, China
国际会议
太原
英文
11-13
2011-02-26（万方平台首次上网日期，不代表论文的发表时间）