摘要
为了提高基于接收信号强度(received signal strength,RSS)的WiFi指纹定位方法的定位精度,提出基于距离和属性加权的室内定位方法(feature weighted K-nearest neighbors,FWKNN)。首先,对不同距离的主特征RSS进行权重分配计算加权距离;然后,用加权距离与离线指纹库进行匹配确定未知位置,对K近邻算法进行了改进;最后,利用卡尔曼滤波精确定位结果,进一步提升定位精度。上述算法结合了距离加权和属性加权的优点,能够更准确的计算各点的距离并选择合适的最近邻点。实验结果表明,FWKNN与KNN和GWKNN相比平均定位精度分别提升了34.6%和27.1%。
In order to improve the positioning accuracy of the WiFi fingerprint position method on the basis of received signal strength(RSS),an indoor position method FWKNN(feature weighted K-nearest neighbors)based on distance and attribute weighting is proposed.First,the weights of the main feature RSS with different distances were assigned to calculate the weighted distance.Then,the weighted distance was used to match the offline fingerprint database to determine the unknown position.Finally,the positioning accuracy was further improved with the accurate positioning results of the Kalman filter.The algorithm unites the benefits of distance weighting and attributes weighting,which can calculate the distance of each point more accurately and select the appropriate nearest neighbor.The experimental results display that the accuracy of FWKNN is raised by 34.6%and 27.1%compared with KNN and GWKNN respectively.
作者
缪颖
朱正伟
诸燕平
MIAO Ying;ZHU Zheng-wei;ZHU Yan-ping(School of Microelectronics and Control Engineering,Changzhou University,Jiangsu Changzhou 213164,China)
出处
《计算机仿真》
北大核心
2022年第8期450-455,共6页
Computer Simulation
关键词
信号接收强度
指纹定位
属性加权
卡尔曼滤波
Received signal strength
Fingerprint location
Attribute weighting
Kalman filter
