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논문검색

Indoor Location Algorithm Based on Kalman Filter and Multi-Source Data Integration

초록

영어

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.

목차

Abstract
 1. Introduction
 2. Onboard Single-Station Passive Direction-Finding and Location Model
 3. Robust Extended Kalman Filter Algorithm
  3.1 Robust EKF Filter Algorithm
  3.2. Robust EKF Algorithm Based on F Distribution Discriminant
 4. Simulation Analysis
  4.1. Simulation Parameter Setting
  4.2. Result and Analysis
 5. Conclusion
 Acknowledgement
 References

저자정보

  • Zhang Ya-qiong Information Engineering school of Yulin University in Yuyang, Yulin, Shaanxi Province, China
  • Li Zhao-xing Information Engineering school of Yulin University in Yuyang, Yulin, Shaanxi Province, China
  • Li Xin School of Urban Design, Wuhan University, Wuhan, China
  • Lv Zhihan-han SIAT, Chinese Academy of Science, Shenzhen, China

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