A new adaptive weighted fusion algorithm for BDS and UWB positioning system
计算机科学
算法
融合
传感器融合
人工智能
语言学
哲学
作者
Rui Xue,Yue Xu,Shuicheng Yan
标识
DOI:10.1117/12.2557493
摘要
In the outdoor scene, most users' localization requirements can be fulfilled through the Beidou system (BDS). However, when facing some challenging environments with serious shadowing and reflection, such as the positioning of seaport containers, the number of observations of the BDS will decrease and the confidence of the observed pseudorange will degrade. In this case, auxiliary data must be added in order to achieve high-precision positioning. Ultra-wide band (UWB) has strong penetrating power and nanosecond temporal resolution, meantime it has the characteristics of highprecision positioning, so this paper adopts the combined positioning method of BDS and UWB to locate targets in the challenging environment. In this paper, appropriate UWB layout location is selected according to the principle of minimum geometric dilution of precision (GDOP). For BDS/UWB co-location system, this paper proposes a new adaptive weighted method based on pseudorange residual detection for weighted least squares (WLS) localization algorithm. Based on the WLS localization algorithm, the Kalman filter (KF) is introduced to improve the WLS performance, so this paper adopts a WLS and KF positioning algorithm (WLS-KF) for BDS/UWB co-location system. The simulation results show that this positioning method can achieve the positioning accuracy below m-level, which can meet the demand of high precision positioning in the complex environment such as seaport containers.