RSS-Based Indoor Localization Using Min-Max Algorithm With Area Partition Strategy
RSS-Based Indoor Localization Using Min-Max Algorithm With Area Partition Strategy
复制标题
基于 RSS 的室内定位，采用最小-最大算法和区域划分策略
DOI：
10.1109/access.2021.3111650
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发表时间：
2021
期刊：
影响因子：
3.9
通讯作者：
Wei Li
中科院分区：
文献类型：
--
作者：
Kuo Yang;Zhonghua Liang;Ren Liu;Wei Li
Min-Max算法作为一种简单的基于接收信号强度（RSS-）的室内定位算法，由于其易于实现而被广泛应用。然而，原始的Min-Max算法仅实现粗估计，将目标节点（TN）视为由测量的RSS值确定的感兴趣区域的几何质心。虽然最近提出了用加权质心代替几何质心的扩展最小-最大（E-Min-Max）方法来解决这一问题，但其对定位精度的提高仍然有限。为了获得更好的定位性能，本文提出了一种带有区域划分策略的改进Min-Max算法（Min-Max- aps）。在该算法中，首先将感兴趣区域划分为四个子区域，每个子区域包含原始感兴趣区域的一个顶点。设计了最小距离差准则，确定顶点与目标节点“最接近”的目标附属子区域。然后将目标节点的位置估计为目标附属子区域的加权质心。由于目标附属子区域小于原始感兴趣区域，因此目标附属子区域的加权质心将比原始感兴趣区域的加权质心更准确。仿真结果表明，所提出的Min-Max- aps算法的定位误差（LE）可降至0.16 m以下，小于E-Min-Max算法的二分之一，也小于原Min-Max算法的七分之一。此外，对于拟议的Min-Max- aps， 90%的LE小于0.38米，而E-Min-Max和原Min-Max的LE分别高达0.49米和1.12米。
Min-Max algorithm was widely used as a simple received signal strength (RSS-) based algorithm for indoor localization due to its easy implementation. However, the original Min-Max algorithm only achieves coarse estimation in which the target node (TN) is regarded as the geometric centroid of the area of interest determined by measured RSS values. Although extended Min-Max (E-Min-Max) methods using weighted centroid instead of geometric centroid were recently proposed to cope with this problem, the improvement in the localization accuracy is still limited. In this paper, an improved Min-Max algorithm with area partition strategy (Min-Max-APS) is proposed to achieve better localization performance. In the proposed algorithm, the area of interest is first partitioned into four subareas, each of which contains a vertex of the original area of interest. Moreover, a minimum range difference criterion is designed to determine the target affiliated subarea whose vertex is “closest” to the target node. Then the target node’s location is estimated as the weighted centroid of the target affiliated subarea. Since the target affiliated subarea is smaller than the original area of interest, the weighted centroid of the target affiliated subarea will be more accurate than that of the original area of interest. Simulation results show that the localization error (LE) of the proposed Min-Max-APS algorithm can drop below 0.16 meters, which is less than one-half of that of the E-Min-Max algorithm, and is also less than one-seventh of that of the original Min-Max algorithm. Moreover, for the proposed Min-Max-APS, 90% of the LE are smaller than 0.38 meters, while the same percentage of the LE are as high as 0.49 meters for the E-Min-Max and 1.12 meters for the original Min-Max, respectively.