Towards Large-Scale RFID Positioning: A Low-cost, High-precision Solution Based on Compressive Sensing
Towards Large-Scale RFID Positioning: A Low-cost, High-precision Solution Based on Compressive Sensing
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DOI：
10.1109/percom.2018.8444586
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发表时间：
2018-03
期刊：
影响因子：
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通讯作者：
Liqiong Chang;Xinyi Li;Ju Wang;Haining Meng;Xiaojiang Chen;Dingyi Fang;Zhanyong Tang;Zheng Wang
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文献类型：
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作者：
Liqiong Chang;Xinyi Li;Ju Wang;Haining Meng;Xiaojiang Chen;Dingyi Fang;Zhanyong Tang;Zheng Wang
基于rfid的定位正在成为仓库和图书馆等地方库存管理的一种有前途的解决方案。然而，现有的解决方案要么对环境噪声过于敏感，要么需要部署大量的参考标签，这将导致昂贵的部署成本，并增加数据冲突的机会。本文介绍了一种基于RFID的新型定位系统CSRP，它具有很高的精度和对环境噪声的鲁棒性，但与最先进的定位系统相比，它依赖的参考标签要少得多。CSRP通过采用抗噪声RFID指纹方案和基于压缩感知的算法来实现这一点，该算法可以使用少量信号测量恢复目标标签的位置。这项工作提供了一套新的分析、算法和启发式方法来指导参考标签的部署，并优化计算开销。我们在使用270个商用RFID标签的部署站点中评估CSRP。实验结果表明，CSRP可以正确识别84.7%的测试项目，使用更少的参考标签，达到与最先进的精度相当的精度。
RFID-based positioning is emerging as a promising solution for inventory management in places like warehouses and libraries. However, existing solutions either are too sensitive to the environmental noise, or require deploying a large number of reference tags which incur expensive deployment cost and increase the chance of data collisions. This paper presents CSRP, a novel RFID based positioning system, which is highly accurate and robust to environmental noise, but relies on much less reference tags compared with the state-of-the-art. CSRP achieves this by employing an noise-resilient RFID fingerprint scheme and a compressive sensing based algorithm that can recover the target tag's position using a small number of signal measurements. This work provides a set of new analysis, algorithms and heuristics to guide the deployment of reference tags and to optimize the computational overhead. We evaluate CSRP in a deployment site with 270 commercial RFID tags. Experimental results show that CSRP can correctly identify 84.7% of the test items, achieving an accuracy that is comparable to the state-of-the-art, using an order of magnitude less reference tags.