基于特征匹配和距离加权的蓝牙定位算法

Bluetooth location algorithm based on feature matching and distance weighting

针对传统i Beacon指纹定位技术中接收信号强度值(RSSI)波动较大、指纹库聚类复杂、存在较大跳变性定位误差等问题,提出一种基于排序特征匹配和距离加权的蓝牙定位算法。在离线阶段,该算法先对RSSI进行加权滑动窗处理,然后根据RSSI向量大小生成排序特征码等值,并与位置坐标等信息组成指纹信息,形成指纹库;在在线定位阶段,根据排序特征向量指纹匹配定位算法和基于距离的最优加权K最邻近法(WKNN)实现室内行人定位。在定位仿真实验中,该算法可以自动根据特征码进行聚类,从而降低了聚类的复杂度,能实现最大误差在0.952 m内的室内行人定位精度。

Focusing on the issues that large fluctuation of Received Signal Strength Indication（ RSSI）,complex clustering of fingerprint database and large positioning error in traditional i Beacon fingerprinting,a new Bluetooth localization algorithm based on sort feature matching and distance weighting was proposed. In the off-line stage,the RSSI vector size was used to generate the sorting characteristic code. The generated code combined with the information of the position coordinates constituted the fingerprint information,to form the fingerprint library. While in the online positioning stage,the RSSI was firstly weighted by sliding window. Then,indoor pedestrian positioning was achieved by using the sort eigenvector fingerprint matching positioning algorithm and distance-based optimal Weighted K Nearest Neighbors（ WKNN）. In the localization simulation experiments,the feature codes were used for automatical clustering to reduce the complexity of clustering with maximum error of 0. 952 m of indoor pedestrian localization.