An indoor location method based on smart phone's RSS fingerprint in four directions
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摘要: 针对传统位置指纹匹配算法只能表征单一维度指纹点特征的问题，提出了一种基于智能手机四向接收信号强度(RSS)指纹的室内定位方法. 该方法通过离线阶段的数据采集、特征提取、接入点(AP)权重分配三个步骤提取了更丰富的指纹点信息，在线阶段使用改进的K最近邻(KNN)分类算法将测试点与指纹点匹配. 在操作系统版本为Android 10的智能手机上使用蓝牙传感器进行实验验证，随机选取30个测试点，得到的实验结果表明：1)四向RSS指纹优于传统的单向RSS指纹，在相同的实验条件下使用四向RSS指纹最高可降低13.4%的定位误差；2)使用四向RSS指纹结合提出的算法，平均定位误差在1.61 m，且响应时间在毫秒级.
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关键词:
- 室内定位 /
- Android系统 /
- 四向接收信号强度(RSS)指纹 /
- 接入点(AP)权重分配 /
- K最近邻(KNN)算法
Abstract: To address the problem that traditional location fingerprint matching algorithm can only represent the characteristics of single dimensional fingerprint points, an indoor locating method based on smartphone's received signal strength (RSS) fingerprint was proposed. In this methods richer fingerprint point information was extracted by data collection, feature extraction and access point (AP) weight assignment in the offline phase, and the improved K nearest neighbor (KNN) algorithm was used to match the test Point with the fingerprint Point in the online phase. In addition, a Bluetooth sensor was used for experimental verification on a smartphone with Android 10 as the operating system version, and 30 test points were randomly selected. Two conclusions can be drawn from the experiment. First, the four-way RSS fingerprint is better than the traditional one-way RSS fingerprint. Under the same experimental conditions, the four-way RSS fingerprint can reduce the positioning error by up to 13.4%. The second is the algorithm proposed by combining the four-way RSS fingerprint, with an average positioning error of about 1.61 meters and a response time of milliseconds. -
表 1 简化后的模糊规则表
IQR Smaller Small Med Big Bigger Noise Std-Dev Smaller Small Med Big Bigger Noise 经典权重 1 0.800 0.600 0.400 0.200 0 改进后的权重 1 0.935 0.526 0.283 0.047 0.001 
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表 2 各方案下的平均定位误差对比
m 方案 K=3 K=4 单向KNN 1.94 1.92 单向WKNN 2.07 2.14 四向KNN 1.68 1.76 四向WKNN 1.83 1.94 本文算法 1.71 1.61
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表 3 本文算法与经典算法的运行时间对比
ms 算法 算法平均耗时 传统NN算法 1.4 传统KNN算法 1.5 传统加权KNN算法 1.7 四向最近邻算法 4.9 四向KNN算法 5.1 四向加权KNN算法 5.4 本文四向算法 5.1
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