标题:Robust Navigation and Positioning Algorithm and Performance Evaluation in Harsh Scenarios
作者:Xiong, Hailiang; Mai, Zhenzhen; Tang, Juan
通讯作者:Xiong, HL
作者机构:[Xiong, Hailiang; Mai, Zhenzhen; Tang, Juan] Shandong Univ, Sch Informat Sci & Engn, Qingdao, Shandong, Peoples R China.
会议名称:IEEE International Conference on Communications (ICC)
会议日期:MAY 20-24, 2019
来源:2019 IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS WORKSHOPS (ICC WORKSHOPS)
出版年:2019
关键词:Kalman filter; strong tracking filter; global position system; tracking;; navigation
摘要:Global positioning system (GPS) is a satellite based tracking system that provides a user with proper real-time three dimensional position and velocity information. However, the measurements from a GPS sensor sometimes are imprecise due to multipath effect and clock offset, which need to be corrected using filtering algorithms. Kalman filter (KF) can reduce errors in a least squares sense and is widely applied in navigation and positioning. Nevertheless, traditional KF needs accurate statistical information about the mobile terminal (MT), otherwise it will result in lower precision and even divergence. In this paper, we present a robust positioning algorithm in harsh scenarios, in which we define residual covariance (RC) as an important index to evaluate the performance of the filter algorithm. We deduce the closed relationship of RC in KF under the cases of 1) inaccurate system model; 2) abrupt change of states, respectively. Then we find that RCs of KF under above two cases both have a bias compared with the desired ones, which indicates KF has poor tracking ability in some harsh environments. However, strong tracking filter (STF) still has strong tracking ability even under above two harsh scenarios because it utilizes a fading factor to adjust the gain matrix in real time so that its RC satisfies the orthogonality principle (OP), which indicates all useful information has been extracted from residual. The theoretical analysis and simulation results demonstrate the effectiveness of strong tracking filter in harsh scenarios.
收录类别:CPCI-S
资源类型:会议论文
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