School of Geomatics Science and Technology, Nanjing Tech University, Nanjing 211800, China.
School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China.
Sensors (Basel). 2019 Nov 7;19(22):4859. doi: 10.3390/s19224859.
Aiming at the problem of indoor environment, signal non-line-of-sight propagation and other factors affect the accuracy of indoor locating, an algorithm of indoor fingerprint localization based on the eight-neighborhood template is proposed. Based on the analysis of the signal strength of adjacent reference points in the fingerprint database, the methods for the eight-neighborhood template matching and generation were studied. In this study, the indoor environment was divided into four quadrants for each access point and the expected values of the received signal strength indication (RSSI) difference between the center points and their eight-neighborhoods in different quadrants were chosen as the generation parameters. Then different templates were generated for different access points, and the unknown point was located by the Euclidean distance for the correlation of RSSI between each template and its coverage area in the fingerprint database. With the spatial correlation of fingerprint data taken into account, the influence of abnormal fingerprint on locating accuracy is reduced. The experimental results show that the locating error is 1.0 m, which is about 0.2 m less than both K-nearest neighbor (KNN) and weighted K-nearest neighbor (WKNN) algorithms.
针对室内环境、信号非视距传播等因素影响室内定位精度的问题,提出了一种基于八邻域模板的室内指纹定位算法。该算法通过分析指纹数据库中相邻参考点的信号强度,研究了八邻域模板匹配和生成方法。该方法将室内环境划分为四个象限,对于每个接入点,选择中心点与其八个邻域在不同象限的接收信号强度指示(RSSI)差值的期望值作为生成参数。然后为不同的接入点生成不同的模板,通过欧氏距离对每个模板与其在指纹数据库中的覆盖区域之间的 RSSI 相关性进行相关,从而定位未知点。该方法考虑了指纹数据的空间相关性,降低了异常指纹对定位精度的影响。实验结果表明,该方法的定位误差为 1.0 m,比 K-最近邻(KNN)和加权 K-最近邻(WKNN)算法分别减少了约 0.2 m。