Data Processing Algorithm of MEMS Inclinometer Based on Improved Sage-Husa Adaptive Kalman Filter
In the actual MEMS inclinometer’s data processing, there are some problems. Such as model error exists in dynamically modeling; the measured signals may be include outliers in complex environment and prior knowledge of the noise statistical rule is insufficient. In order to solve these problems, an improved Sage-Husa adaptive Kalman filter is proposed. According to the model error, it adds a weighting function to the step variance matrix of the filter algorithm after judging the filter whether abnormal or not, which is used to inhibit divergent of the filter. And with outliers’ problems, to achieve the purpose of restraining outliers, it keeps up new information original nature by using a fixed function weighted in the new information sequence of the filter algorithm equation. Finally, the experiment results show that this method can improve the robustness of the filter, inhibit outliers, and at the same time, make the variance of the output signal of MEMS inclinometer one order of magnitude smaller.
MEMS inclinometer Sage-Husa adaptive Kalman filter ARMA model Fault-tolerant to outlier Data processing
BAI Yongqiang HAN Junhui QI Xianghai
School of Automation, Beijing Institute of Technology, Beijing 100081 Key Laboratory of Complex Syst School of Automation, Beijing Institute of Technology, Beijing 100081 Key Laboratory of Complex Syst Institute of State 157 factory, Chengdu 611930, China
国际会议
The 31st Chinese Control Conference(第三十一届中国控制会议)
合肥
英文
3702-3707
2012-07-01（万方平台首次上网日期，不代表论文的发表时间）