Kernel-based positioning in Wireless Local Area Networks
Kernel-based positioning in Wireless Local Area Networks
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DOI：
10.1109/tmc.2007.1017
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发表时间：
2007-06-01
影响因子：
7.9
通讯作者：
Venetsanopoulos, Anastasios N.
中科院分区：
文献类型：
--
作者：
Kushki, Azadeh;Plataniotis, Konstantinos N.;Venetsanopoulos, Anastasios N.
最近基于位置的服务(LBSS)的激增要求开发有效的室内定位解决方案。在这种情况下，由于无线局域网基础设施的无处不在，在硬件和安装成本方面，无线局域网(WLAN)定位是一种特别可行的解决方案。本文从三个方面研究了基于接收信号强度(RSS)的室内无线局域网定位问题。首先，我们证明，由于RSS特征在空间上的可变性，空间局部化的定位方法可以改善定位结果。其次，我们探讨了用于定位的接入点(AP)选择问题，并论证了该领域进一步研究的必要性。第三，提出了一种核化距离计算算法，用于比较RSS观测值和RSS训练记录。实验结果表明，该系统比目前广泛使用的K近邻方法和基于直方图的方法提高了17%(0.56m)。
The recent proliferation of Location-Based Services (LBSs) has necessitated the development of effective indoor positioning solutions. In such a context, Wireless Local Area Network (WLAN) positioning is a particularly viable solution in terms of hardware and installation costs due to the ubiquity of WLAN infrastructures. This paper examines three aspects of the problem of indoor WLAN positioning using received signal strength (RSS). First, we show that, due to the variability of RSS features over space, a spatially localized positioning method leads to improved positioning results. Second, we explore the problem of access point (AP) selection for positioning and demonstrate the need for further research in this area. Third, we present a kernelized distance calculation algorithm for comparing RSS observations to RSS training records. Experimental results indicate that the proposed system leads to a 17 percent (0.56 m) improvement over the widely used K-nearest neighbor and histogram-based methods.