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Hybrid Adaptive Cubature Kalman Filter with Unknown Variance of Measurement Noise
Yuepeng Shi1, Xianfeng Tang2, Xiaoliang Feng3
1College of Intelligent Manufacturing and Automation, Henan University of Animal Husbandry and Economy, Zhengzhou 450011, China. syp@hnuahe.edu.cn.
Abstract:
This paper is concerned with the filtering problem caused by the inaccuracy variance of measurement noise in real nonlinear systems. A novel weighted fusion estimation method of multiple different variance estimators is presented to estimate the variance of the measurement noise. On this basis, a hybrid adaptive cubature Kalman filtering structure is proposed. Furthermore, the information filter of the hybrid adaptive cubature Kalman filter is also studied, and the stability and filtering accuracy of the filter are theoretically discussed. The final simulation examples verify the validity and effectiveness of the hybrid adaptive cubature Kalman filtering methods proposed in this paper.
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