Reducing the Complexity of Fingerprinting-Based Positioning using Locality-Sensitive Hashing
Reducing the Complexity of Fingerprinting-Based Positioning using Locality-Sensitive Hashing
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DOI：
10.1109/ieeeconf44664.2019.9048657
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发表时间：
2019-11
期刊：
影响因子：
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通讯作者：
Larry L Tang;Ramina Ghods;Christoph Studer
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文献类型：
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作者：
Larry L Tang;Ramina Ghods;Christoph Studer
基于信道状态信息（CSI）指纹识别的无线发射机的定位在室内以及室外场景中得到广泛使用。指纹定位首先建立一个数据库，其中包含具有测量位置信息的CSI。然后在该数据库中搜索最相似的CSI以近似无线发射机的位置。在本文中，我们研究了局部敏感哈希（LSH）的功效，以降低传统指纹定位系统所需的最近邻搜索（NNS）的复杂性。更具体地说，我们提出了一个低复杂度和内存效率的LSH函数的基础上的总和到一（STOne）变换和使用近似哈希匹配。我们评估了视线（LoS）和非LoS通道方法的准确性和复杂性（在搜索次数和存储要求方面），我们表明LSH能够实现低复杂度的指纹定位，其准确性与依赖于精确NNS或深度神经网络的方法相当。
Localization of wireless transmitters based on channel state information (CSI) fingerprinting finds widespread use in indoor as well as outdoor scenarios. Fingerprinting localization first builds a database containing CSI with measured location information. One then searches for the most similar CSI in this database to approximate the position of wireless transmitters. In this paper, we investigate the efficacy of locality-sensitive hashing (LSH) to reduce the complexity of the nearest neighbor- search (NNS) required by conventional fingerprinting localization systems. More specifically, we propose a low-complexity and memory efficient LSH function based on the sum-to-one (STOne) transform and use approximate hash matches. We evaluate the accuracy and complexity (in terms of the number of searches and storage requirements) of our approach for line-of-sight (LoS) and non-LoS channels, and we show that LSH enables low-complexity fingerprinting localization with comparable accuracy to methods relying on exact NNS or deep neural networks.