基于稳定AP选择的动态室内定位方法  

Dynamic indoor positioning method based on stable AP selection

作　　者：魏军 罗恒[1] 倪启东 陈明哲 WEI Jun;LUO Heng;NI Qidong;CHEN Mingzhe(School of Electronic and Information Engineering,Suzhou University of Science and Technology,Suzhou 215009,China)

机构地区：[1]苏州科技大学电子与信息工程学院,江苏苏州215009

出　　处：《微电子学与计算机》2024年第1期37-44,共8页Microelectronics & Computer

基　　金：国家自然科学基金(61602334)。

摘　　要：在室内复杂多变环境下,基于接收信号强度指示(Received Signal Strength Indication,RSSI)的位置指纹算法得到了广泛研究。其中,在线阶段的匹配算法通常采用加权K近邻(Weighted K-Nearest Neighbor,WKNN)算法,但该算法往往采用固定k值方法存在较大的定位误差,具有一定的局限性,并且离线阶段构建位置指纹数据库时并没有考虑到无线接入点(Access Point,AP)信号的波动性。因此,存在大量不同AP的冗余信息,对定位效果产生较大影响。针对上述问题,提出一种基于稳定AP选择的动态室内定位方法。首先,通过高斯滤波对RSSI值进行预处理,滤除随机干扰;然后,通过优选AP算法计算AP的稳定度,筛选出关键AP用于定位;最后,利用距离阈值动态调整k值,并对权重系数进行改善,实现了对WKNN算法的改进。实验结果表明,基于稳定AP选择的动态室内定位方法可以有效去除冗余AP信息,并实现动态k值方案,在定位精度上优于K近邻(K-Nearest Neighbors,KNN)算法、加权K近邻算法和改进的加权K近邻算法,平均定位误差分别降低了26.13%、21.29%和9.89%,定位误差在1.5 m内的累积分布概率达到了60.41%,分别提升了25%、16.66%和8.33%,定位效果提升明显。In the complex and changeable indoor environment,the positioning fingerprint algorithm based on Received Signal Strength Indication(RSSI)has been widely studied.The matching algorithm in the online stage usually uses the Weighted K-Nearest Neighbor(WKNN)algorithm,but the algorithm often uses the fixed k value method,which has large position errors and certain limitations.In addition,the volatility of Access Point(AP)signals is not taken into account when building the positioning fingerprint database in the offline stage.Therefore,there is a lot of redundant information of different APs,which has a great impact on the positioning effect.To solve these problems,a dynamic indoor positioning method based on stable AP selection is proposed.Firstly,the RSSI value is preprocessed by Gaussian filtering to eliminate the random interference.Then,the stability of AP is calculated by optimizing AP algorithm,and the key AP is selected for positioning.Finally,the distance threshold is used to dynamically adjust the value of k,and the weight coefficient is improved,so the WKNN algorithm is improved.The experimental results show that the dynamic indoor positioning method based on stable AP selection can effectively remove redundant AP information and realize the dynamic k-value scheme.It is superior to the K-Nearest Neighbor(KNN)algorithm,the weighted K-nearest neighbor algorithm and the improved weighted K-nearest neighbor algorithm in positioning accuracy.The average positioning error is reduced by 26.13%,21.29%and 9.89%respectively.The cumulative distribution probability of positioning error within 1.5 m reaches 60.41%,which is increased by 25%,16.66%and 8.33%respectively,and the positioning effect is improved obviously.

关 键 词：室内定位 优选AP 信号强度 加权K近邻算法 信号波动 指纹匹配 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]