Luo Qinghua, Yang Kexin, Yan Xiaozhen, Li Jianfeng, Wang Chenxu, Zhou Zhiquan
School of Information Science and Engineering, Harbin Institute of Technology, Weihai 264209, China.
Shandong Institute of Shipbuilding Technology, Ltd., Weihai 264209, China.
Sensors (Basel). 2022 Aug 15;22(16):6085. doi: 10.3390/s22166085.
As a classic positioning algorithm with a simple principle and low computational complexity, the trilateration positioning algorithm utilizes the coordinates of three anchor nodes to determine the position of an unknown node, which is widely applied in various positioning scenes. However, due to the environmental noise, environmental interference, the distance estimation error, the uncertainty of anchor nodes' coordinates, and other negative factors, the positioning error increases significantly. For this problem, we propose a new trilateration algorithm based on the combination and K-Means clustering to effectively remove the positioning results with significant errors in this paper, which makes full use of the position and distance information of the anchor nodes in the area. In this method, after analyzing the factors affecting the optimization of the trilateration and selecting optimal parameters, we carry out experiments to verify the effectiveness and feasibility of the proposed algorithm. We also compare the positioning accuracy and positioning efficiency of the proposed algorithm with those of other algorithms in different environments. According to the comparison of the least-squares method, the maximum likelihood method, the classical trilateration and the proposed trilateration, the results of the experiments show that the proposed trilateration algorithm performs well in the positioning accuracy and efficiency in both light-of-sight (LOS) and non-light-of-sight (NLOS) environments. Then, we test our approach in three realistic environments, i.e., indoor, outdoor and hall. The experimental results show that when there are few available anchor nodes, the proposed localization method reduces the mean distance error compared with the classical trilateration, the least-squares method, and the maximum likelihood.
作为一种原理简单、计算复杂度低的经典定位算法，三边测量定位算法利用三个锚节点的坐标来确定未知节点的位置，广泛应用于各种定位场景。然而，由于环境噪声、环境干扰、距离估计误差、锚节点坐标的不确定性等负面因素，定位误差显著增加。针对这个问题，本文提出了一种基于组合和K均值聚类的新型三边测量算法，以有效去除存在显著误差的定位结果，该算法充分利用了区域内锚节点的位置和距离信息。在该方法中，在分析影响三边测量优化的因素并选择最优参数后，我们进行实验以验证所提算法的有效性和可行性。我们还在不同环境下将所提算法的定位精度和定位效率与其他算法进行比较。根据最小二乘法、最大似然法、经典三边测量法和所提三边测量法的比较，实验结果表明所提三边测量算法在视距（LOS）和非视距（NLOS）环境下的定位精度和效率方面均表现良好。然后，我们在室内、室外和大厅这三种现实环境中测试了我们的方法。实验结果表明，当可用锚节点较少时，与经典三边测量法、最小二乘法和最大似然法相比，所提定位方法降低了平均距离误差。
