一种改进的WKNN匹配算法

An improved WKNN matching algorithm

在WiFi室内定位方法中,基于接收信号强度(RSSI)离线指纹数据库的加权K最邻近点(WKNN)算法得到了深入研究,但目前的WKNN算法未考虑实测数据维度高、无效缺省数据多等特点,不利于匹配定位精确度的提高。为此,在对实测RSSI指纹向量按照由大到小进行排序的基础上,只选取大于设定RSSI阈值的有效RSSI指纹数据进行后续的匹配;按照欧式距离的统计量自适应调整K值;按照欧式距离的均值,调整高斯权重系数。实验结果表明,与未改进的WKNN算法相比,改进后的WKNN算法定位精确度更高。

The Weighted K Nearest Neighbors(WKNN)algorithm based on the off-line Received Signal Strength Indication(RSSI)fingerprint database has been studied intensively in the indoor positioning methods based on the received Wireless Fidelity(WiFi)signal.However,the specifications of the received RSSI fingerprint data,such as the high dimension and many invalid default RSSI values,have not been addressed in the existing WKNN algorithm,which is not good for improving its positioning accuracy.Aiming at the problems of the existing WKNN algorithm,the received RSSI values will be sorted in descending order,and the RSSI values larger than the preset threshold are selected to match with the off-line RSSI fingerprint database in the following steps.Then,the K value is determined on line adaptively by the statistics of the Euclidean distances.Finally,the Gaussian weights are updated by the means of the Euclidean distances.The experiment results show that the improved WKNN algorithm achieves more accurate positioning performance than the existing WKNN one.