Dude, where's my card?

Abstract

RFIDs are emerging as a vital component of the Internet of Things. In 2012, billions of RFIDs have been deployed to locate equipment, track drugs, tag retail goods, etc. Current RFID systems, however, can only identify whether a tagged object is within radio range (which could be up to tens of meters), but cannot pinpoint its exact location. Past proposals for addressing this limitation rely on a line-of-sight model and hence perform poorly when faced with multipath effects or non-line-of-sight, which are typical in real-world deployments. This paper introduces the first fine-grained RFID positioning system that is robust to multipath and non-line-of-sight scenarios. Unlike past work, which considers multipath as detrimental, our design exploits multipath to accurately locate RFIDs. The intuition underlying our design is that nearby RFIDs experience a similar multipath environment (e.g., reflectors in the environment) and thus exhibit similar multipath profiles . We capture and extract these multipath profiles by using a synthetic aperture radar (SAR) created via antenna motion. We then adapt dynamic time warping (DTW) techniques to pinpoint a tag's location. We built a prototype of our design using USRP software radios. Results from a deployment of 200 commercial RFIDs in our university library demonstrate that the new design can locate misplaced books with a median accuracy of 11~cm.

LTE radio analytics made easy and accessible

“…[14] build antenna arrays using software radios synchronized with a reference signal. PinIt [29] is an RFID localization system that combines SAR with a deployment of reference RFIDs to achieve highly accurate localization.…”

“…However, LTE runs at much lower frequencies than Wi-Fi (700 MHz as opposed to 2.4 GHz), and hence an LTE antenna array will be 4 to 5 times more bulky than a comparable Wi-Fi array. The second approach uses synthetic aperture radar (SAR) [29], which uses a single movable antenna to emulate a virtual array of many antennas. As the antenna moves, it traces the locations of antenna elements in a virtual array.…”

“…The quantity a f (t, θ, φ) captures the relative motion of the transmitter and receiver, and is independent of the wireless channels. For e.g., if the antenna moves along a straight line (i.e., linear SAR) a f (t, θ, φ) is defined as: a f (t, θ, φ) = e −j 2πf c x(t)cos(φ) , where x(t) is the antenna location at time t, and f is the frequency of the signal [29]. Similarly, if the antenna rotates with radius r (i.e., circular SAR) a f (t, θ, φ) = e −j 2πf c rcos(φ−φ 0 (t)) , where φ0(t) is the angular position of the antenna at time t (see Fig.…”

“…To this end, LTEye employs RF fingerprints [31,29] to map C-RNTIs between logs. LTEye defines a user's fingerprint as the set of observed multipath profiles at each LTEye sniffer (See §6.1).…”

“…Second, recent localization techniques using synthetic aperture radar (SAR) are less bulky, but are limited to signals transmitted and received by the same node (e.g. radar [12] or RFID backscatter [29] systems) and therefore do not apply to LTE signals. LTEye provides the best of both these solutions by extending SAR localization techniques to operate over communication signals as opposed to backscatter or radar signals.…”