基于UWB的加权同心圆聚类室内定位方法

为了降低基于超宽带（UWB）测距中的非视距（NLoS）误差的影响，引入了一种基于遗传算法 反向传播神经网络（GA-BP）的UWB测距误差识别与优化方法，能够识别NLoS传播链路下的数据，对NLoS传播链路下测距误差和系统偏差进行校正，最后对测距结果使用卡尔曼滤波（KF）优化。在此基础上，针对测距误差导致的多边定位无交点或多交点问题，提出了一种加权同心圆聚类定位（WCCGT）方法，通过加权同心圆生成（WCCG）解决无交点问题，再采用均值漂移聚类定位方法实现定位解算，以提高定位精度。实验结果表明，改进的测距优化方法有效减小了NLoS传播链路下的测距误差，基于UWB的测距精度提升了60%以上；通过静态定位实验和动态实验分析，将WCCGT方法定位结果与最小二乘（LS）方法进行了比较，本文方法能够在NLoS环境下达到10.78 cm的定位精度，定位性能提升了17.32%。

Weighted concentric circle generation clustering indoorpositioning method based on UWB

To mitigate the influence of non line of sight (NLoS) errors in ultra-wideband (UWB) ranging, this study presents a method that utilizes a genetic algorithm backpropagation neural network (GA-BP) for error identification and optimization. This method effectively detects and rectifies ranging errors and system deviations occurring in the NLoS propagation link, and subsequently improves the ranging outcomes through the application of Kalman filtering (KF). On this basis, this paper proposes a weighted concentric circle clustering localization (WCCGT) method to address the problem of no intersection or multiple intersection points in multilateral positioning caused by ranging errors. The method solves the problem of no intersection points through weighted concentric circle generation (WCCG). Then, it uses the mean shift clustering localization method to achieve a localization solution and improve localization accuracy. The experimental results show that the improved ranging optimization method effectively reduces the ranging error in the NLoS propagation link, and the ranging accuracy based on UWB is improved by more than 60%. Analyze through static positioning experiments and dynamic experiments, the positioning results of the WCCGT method were compared with the least squares (LS) method. The proposed method can achieve a positioning accuracy of 10.78 cm in NLoS environments, and the positioning performance has been improved by 17.32%.