改进DBSCAN聚类的信道状态信息定位算法

近年来，利用WiFi信道状态信息的无线信号在室内定位、跌倒检测、身份识别等场景都发挥了重要应用价值。然而，复杂环境下多径效应的影响使得指纹定位的精度仍有待提高。针对这一问题，本文在降噪时提出了一种基于密度的自适应聚类算法，并在定位阶段联合动态加权K邻近算法进行匹配。首先，使用Hampel算法去除幅值信息的离群点；然后，将改进的DBSCAN算法自动调节参数对数据聚类；最后，用动态加权K邻近算法进行指纹库与实时定位点的匹配。仿真实验表明：在约5×10m2的定位区域内，DBSCAN算法的平均定位精度达到1.579m，其中定位精度在2m内的占比相对于传统指纹法提高了42.9%。

Channel State Information localization based on improved DBSCAN clustering algorithm

In recent years, wireless signals using WiFi Channel State Information have played important roles in scenarios such as indoor positioning, fall detection, and identification. However, the impact of multipath effects in complex environments makes the accuracy of fingerprint positioning to be improved. To solve this problem, this paper proposes an improved Density-Based Spatial Clustering of Applications with Noise during the process of noise reduction combined with Enhanced weighted K-nearest neighbor algorithm in the online stage. First, the Hampel algorithm is used to remove outliers of the amplitude information; then, the improved DBSCAN algorithm automatically adjusts the parameter to cluster data; finally, the Enhanced weighted K-nearest neighbor algorithm is used to match the real-time positioning points from the fingerprint database. The experimental results show that the average positioning accuracy of the DBSCAN algorithm reaches 1.579m in a positioning area of about 5×10m2, and the percentage of error within 2m is increased by 42.9% compared to the traditional fingerprint method.