Localization of RFID Tags Using Stochastic Tunneling

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.