Design of an Adaptive Two-step Kalman Filter Based on Fuzzy Logic
JIN Mei1, ZHANG Zhi-fu1, JIN JU2, YU Guo-hui3, LI Wen-chao1
1.Measurement Technology and Instrumentation Key Lab of Hebei Province,Yanshan University, Qinhuangdao,Hebei 066004， China;
2.School of Civil Engineering, Hebei University of Technology, Tianjin 300401, China;
3.Audio-Visual Machinery Research Institute, Qinhuangdao, Hebei 066004, China
Abstract：To analyze the modeling of the inertial/magnetic tracking system, the centralized Kalman filter algorithm depending on PC is often used to process the data. Because this algorithm is complex and the data processing speed is slow, it is difficult to realize high-speed motion tracking in the embedded system. An adaptive two-step Kalman filter method based on fuzzy logic for real-time motion tracking of human body is proposed. The parameters of Kalman filter are self- adjusted according to the different motion states of human body. The method can not only improve the estimation accuracy and fault-tolerant performance of the system to a certain degree, but also reduce the matrix dimension.
［1］ Welch G, Bishop G, Vicci L, et al. The HiBall tracker:High-performance wide-area tracking for virtual and augmented environment ［C］ // VRST ’39, London, UK, 1999, 1-10.
［2］ Roetenberg D, Slycke P, Ventevogel A, et al. A portable magnetic position and orientation tracker［J］.Sensors and Actuators A, 2007,135:426-432
［3］ Sutton R. Adaptive tuning of a Kalman filter via fuzzy logic for an intelligent AUV navigation system［J］.Control engineer practice, 2004, 12:1531-1539
［4］ Chaer W S, Bishop R H. A Mixture-of-Experts Framework for Adaptive Kalman Filtering［J］. IEEE TRANSACTIONS ON SYSTEMS, MAN AND CYBERNETICS—PART B: CYBERNETICS, 1997, 27(3): 452-464.
［5］ Escamilla-Ambrosio P J, Mort N. Multisensor Data Fusion Architecture Based on Adaptive Kalman Filters and Fuzzy logic Performance Assessment［C］ //ISIF,Annapolis,USA,2002, 1542-1549.
［6］ Young A D, Ling M J.Distributed Estimation of linear Acceleration for Improved Accuracy in Wireless Inertial Motion Capture［C］ //IPSN’10, Stockholm, Sweden, 2010, 256-267.
［7］ Yang Y,Miao L J. Adaptive federal Kalman filtering for SINS/GPS Integrated System［J］. Journal of Beijing Institute of Technology, 2003, 12(4): 371-375.
2014, Vol. 35 
