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Indoor Location Algorithm Based on Kalman Filter and Multi-Source Data Integration
Zhang Ya-qiong,Li Zhao-xing,Li Xin,Lv Zhihan-han 보안공학연구지원센터 2016 International Journal of Database Theory and Appli Vol.9 No.4
For onboard single-station passive direction-finding and location, if there is any abnormal error in the observation data, the extended Kalman filter (EKF) algorithm adopted thereby will cause inaccurate location result. In order to improve algorithm robustness, the robust equivalent gain matrix is constructed according to the standardized prediction residual error and the robust EKF algorithm is applied to the onboard single-station passive direction-finding and location. In allusion to the low efficiency of the robust EKF algorithm, the single-station passive location algorithm based on the improved extended Kalman filter is proposed in this article on the basis of combining F distribution statistic, and meanwhile single abnormal error and continuous abnormal error are added in the observation value to test the algorithm resistance to different abnormal errors. The simulation shows that the algorithm proposed in this article can well weaken the influence of abnormal errors on position estimation and the algorithm based on F distribution discriminant can improve location efficiency.
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