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Localization of RFID Tags Using Stochastic Tunneling

机译：使用随机隧道技术对RFID标签进行本地化

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This paper presents a novel localization scheme in the 3D wireless domain that employs cross correlation in backscattered signal power from a cluster of radio frequency identification (RFID) tags to estimate their location. Spatially co-located RFID tags, energized by a common tag reader, exhibit correlation in their received signal strength indicator (RSSI) values. Hence, for a cluster of RFID tags, the posterior distribution of their unknown radial separation is derived as a function of the measured RSSI correlations between them. The global maxima of this posterior distribution represent the actual radial separation between the RFID tags. The radial separations are then utilized to obtain location estimates of the tags. However, due to the nonconvex nature of the posterior distribution, deterministic optimization methods that are used to solve true radial separations between tags provide inaccurate results due to local maxima, unless the initial radial separation estimates are within the region of attraction of its global maximum. The proposed RFID localization algorithm called LOCalization Using Stochastic Tunneling (LOCUST) utilizes constrained simulated annealing with tunneling transformation to solve this nonconvex posterior distribution. The tunneling transformation allows the optimization search operation to circumvent or "tunnel” through ill-shaped regions in the posterior distribution resulting in faster convergence to the global maximum. Finally, simulation results of our localization method are presented to demonstrate the theoretical conclusions.
机译：本文提出了一种3D无线域中的新颖定位方案，该方案在射频识别（RFID）标签集群的反向散射信号功率中采用互相关来估计其位置。由共同标签读取器供电的空间共处位置的RFID标签在其接收信号强度指示器（RSSI）值中显示出相关性。因此，对于一组RFID标签，其未知径向间隔的后验分布是根据它们之间测得的RSSI相关性得出的。此后分布的全局最大值表示RFID标签之间的实际径向距离。然后利用径向间隔来获得标签的位置估计。但是，由于后验分布的非凸性质，用于解决标签之间真实径向间距的确定性优化方法由于局部最大值而无法提供准确的结果，除非初始径向间距估算值位于其全局最大值的吸引范围内。所提出的RFID定位算法称为使用随机隧道的LOCaization（LOCUST），利用带隧道变换的约束模拟退火来解决这种非凸后验分布。通过隧道转换，优化搜索操作可以绕过或分布在后部分布中的不规则区域，从而更快地收敛到全局最大值，最后，给出了我们的定位方法的仿真结果，以证明理论结论。

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