• 网络与通信 •

### 基于阈值筛选的室内定位优化算法

1. (中北大学信息与通信工程学院，山西太原030051)
• 出版日期:2022-05-07 发布日期:2022-05-07
• 作者简介:柴晨境(1996—)，男，山西临汾人，硕士研究生，研究方向：基于超宽带的室内定位算法，工业上位机操作系统设计，E-mail: 664388303@qq.com; 刘宾（1979—），男，山东济宁人，教授，硕士生导师，博士，研究方向：光电检测及图像处理，光场成像，嵌入式及动态测试，E-mail: liubin414605032@163.com; 潘晋孝（1966—），男，山西万荣人，教授，硕士生导师，博士，研究方向：矩阵理论，随机过程，小波理论，现代优化理论，泛函分析，图像信息处理及增强，E-mail: panjx@nuc.edu.cn。
• 基金资助:
山西省科技重大专项(20191102010)

### Indoor Positioning Optimization Algorithm Based on Threshold Filtering

1. (School of Information and Communication Engineering, North University of China, Taiyuan 030051, China)
• Online:2022-05-07 Published:2022-05-07

Abstract: Aiming at the defects of indoor ultra-wide band (UWB) positioning technology, such as poor positioning effect and inaccurate positioning in complex occlusion environment, this paper proposes a hybrid positioning method based on Chan algorithm and particle swarm optimization algorithm. First, the Chan algorithm is used to obtain the initial estimated position coordinates of the positioning tag, and in a non-line-of-sight (NLOS) environment, a threshold θ is set to filter the position coordinates calculated by the Chan algorithm. The distance difference received by the known base station is summed with the distance difference between different base stations obtained by the tag position information calculated by Chan algorithm. If the sum of the differences is less than the threshold, the position coordinates are directly output. Otherwise, the position coordinates are used as the initial value of the particle swarm algorithm for iteration. Optimization keeps track of individual extreme values and local extreme values, updates individual positions and speeds, and finds the global optimal solution before outputting. The simulation results and the actual field experiment results show that compared with a single algorithm, the hybrid positioning algorithm proposed in this paper improves the positioning accuracy of 27%~31% in the non-line-of-sight environment. The convergence speed is fast, the algorithm complexity is low, and it meets the requirements of indoor positioning.