| 基于区域分类和电子地图的室内定位研究 |
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| 引用本文： | 牛福娟,滕凯,杨行,邢宗义. 基于区域分类和电子地图的室内定位研究[J]. 铁路计算机应用, 2020, 29(9): 1 |
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| 作者姓名： | 牛福娟 滕凯 杨行 邢宗义 |
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| 作者单位： | 南京理工大学 自动化学院，南京 210094 |
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| 基金项目： | 国家重点研发计划课题（2017YFB1201203） |
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| 摘 要： | 为辅助盲人独立乘坐地铁出行，解决传统室内定位法易产生定位点跳跃和定位实时性差等问题，提出一种融合区域分类和电子地图的方法。该方法分为离线区域分类和在线定位2个阶段:在离线阶段通过迭代寻优，为蓝牙传感器产生的RSSI序列寻找最佳高斯滤波模板，采用滤波后的序列均值构建位置指纹库，并利用支持向量机模型对位置指纹库进行1级分类；在线阶段，采用滑动窗口对待定位区域进行2级分类，在窗口范围内采用基于欧氏距离的KNN算法求取位置坐标，并利用电子地图的路径层信息对定位坐标纠偏，进一步控制误差范围，提高定位效率。实验表明:该滤波方法将定位精度提高近4%，使用该定位算法最终能达到1.59 m定位精度。
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| 关 键 词： | 室内定位 高斯滤波 支持向量机 滑动窗口 电子地图 |
| 收稿时间： | 2020-02-18 |
Research on indoor location based on region classification and electronic map |
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| Affiliation: | School of Automation, Nanjing University of Science and Technology, Nanjing 210094, China |
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| Abstract: | In order to assist blind people to travel independently by subway and solve the problems of hopping discontinuity of location points and poor real-time location in traditional indoor location methods, a method by fusing region and electronic map is proposed. The method can be divided into two stages: offline region classification and online location. In the stage of offline region classification, the best Gauss filtering template is searched for RSSI sequences generated from Bluetooth sensors by iterative refinement and the filtered sequence means are used to construct location fingerprint database, and the support vector machine model is used to classify the location fingerprint database at the first level. In the stage of online location, a sliding window is used to classify the regions to be located at the second level, a KNN algorithm based on Euclidean distance is used to calculate the location coordinates within the range of the sliding window, and the path layer data in a electronic map is used to reduce the deviation of location coordinates so as to further control the error margin and improve the efficiency of location. The tests in a subway station hall show that the filtering method can improve the accuracy of location by nearly 4% and finally the accuracy of location can reach 1.59 m by using this location algorithm. |
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