Reducing fingerprint collection for indoor localization
Reducing fingerprint collection for indoor localization
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DOI：
10.1016/j.comcom.2015.09.022
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发表时间：
2016-06-01
影响因子：
6
通讯作者：
Chen, Ai
中科院分区：
文献类型：
--
作者：
Gu, Zhuan;Chen, Zeqin;Chen, Ai
典型的基于WiFi的室内定位技术通过将接收信号强度指示器(RSSI)与存储的指纹进行比较并找到最接近的匹配来估计设备位置。然而，采集指纹是出了名的费力和昂贵。在不引入重大错误的情况下减少指纹采集和恢复丢失的数据是一项具有挑战性的工作。提出了一种新的基于压缩感知的缺失指纹恢复方法。指纹的隐含结构和冗余特征被揭示在一个合并矩阵中。指纹的空间和时间相关性导致合并矩阵的排序较小。采用稀疏秩奇异值分解(SRSVD)方法，有效地降低了WiFi信号的多径效应带来的干扰。我们进一步提出将SRSVD算法与K-近邻(KNN)算法相结合来处理矩阵中缺失的列或行。实验结果表明，该方法只需采集一半的指纹，就能恢复全部指纹信息，错误率低于6.6%。即使只需要5%的数据，该方法也可以在不损失定位精度的情况下恢复误码率低于14%的信息。(C)2015爱思唯尔B.V.保留所有权利。
A typical WiFi-based indoor localization technique estimates a devices location by comparing received signal strength indicator (RSSI) against stored fingerprints and finding the closest matches. However, the collection of fingerprints is notoriously laborious and costly. It is challenging to reduce fingerprint collection and recover missing data without introducing significant errors. In this article, a novel approach based on compressive sensing is presented for recovering absent fingerprints. The hidden structure and redundancy characteristics of fingerprints are, revealed in a merging matrix. The spatial and temporal correlations of fingerprints result in a small rank of the merging matrix. The Sparsity Rank Singular Value Decomposition (SRSVD) method is used to effectively reduce the interference caused by the multipath effect of the WiFi signal. We further propose to combine SRSVD with the K-Nearest Neighbor (KNN) algorithm to deal with missing columns or rows in the matrix. Experimental results show that with only half of the fingerprints, our approach can recover all the fingerprint information with error rate below 6.6%. Even with only 5% of the data, the approach can recover the information with error rate below 14%, without loss of localization accuracy. (C) 2015 Elsevier B.V. All rights reserved.