针对基于RSSI测距定位中偶然性测距误差和设备误差对定位结果影响较大这一问题,提出了一种误差离群去约束的优化方法.通过对三边定位结果使用K-means算法进行离群分析,得到对定位结果影响较大的几个锚节点,从而对数据进行处理.针对实际系统使用RSSI测距模块测得多组实验数据,并用Matlab软件进行仿真分析,将测距误差离群去约束模型与传统均值模型使用最小二乘法在不同距离下进行定位对比.仿真结果表明,当引入不同数量误差锚节点时,前者在定位精度方面均有0. 1 ～ 0. 2 m2提升,同时算法也具有很好的鲁棒性.%In view of the fact that accidental ranging error and equipment error have a great influence on the positioning result based on RSSI ranging,an error outlier de-regulation model is proposed. Using the K-means algorithm and trilateral positioning results for outlier analysis,several anchor nodes which have a large impact on the positioning result are obtained and the data is processed. The RSSI ranging module was used to measure the actual system,and many sets of experimental data were obtained,and simulation analysis was performed with Matlab. The outlier error de-nesting model and the traditional average model were compared using the same positioning algorithm at different distances. The simulation results show that when different number of error anchor nodes are added,the former has an improvement of positioning accuracy of 0. 1 ～ 0. 2 m2,and the robustness of the algorithm is very good.
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