基于移动终端和大数据的自动化定位研究

Research on automatic positioning based on mobile terminal and big data

为提高移动终端的自动化定位精度,提出一种基于细粒度WIFI指纹地图+改进KNN的移动终端定位方法。其中,首先采用DBSCAN聚类算法对移动终端的位置数据进行预处理,为后续的定位奠定基础;其次,提出基于细粒度WIFI指纹地图生成指纹地图,进而提高后续移动终端的定位精度;最后采用改进KNN算法实现移动终端的定位。通过MATLAB软件进行实验仿真,仿真结果表明,与其他基线模型相比,本研究构建的指纹地图模型能够完成更高精度的定位;与粗粒度指纹地图相比,本研究所在于的细粒度指纹地图算法有着更高的精度,定位误差仅为8.29 m。以上实验结果验证了本研究构建的定位方法的可行性和合理性,具有一定的实用价值。

In order to improve the automatic positioning accuracy of mobile terminals, a mobile terminal positioning method based on fine-grained WiFi fingerprint map is proposed. Firstly, DBSCAN clustering algorithm is used to preprocess the location data of the mobile terminal;Secondly, the improved KNN algorithm is used to realize the location of mobile terminal. The simulation results show that compared with other baseline models, the fingerprint map model constructed in this study can achieve higher precision positioning;Compared with the coarse-grained fingerprint map, the fine-grained fingerprint map algorithm in this study has higher accuracy, and the positioning error is only 8.29 m. The above experimental results verify the feasibility and rationality of the positioning method constructed in this study, and have certain practical value.