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This paper presents a realistic feasibility of radio frequency identification (RFID) based self-localization of objects in real environments using simple infrastructure composed of chipless tags. These tags are designed as arrays of dielectric resonators (DR) in order to overcome the range limitations of the traditional chipless tags, where the DR array structure of the tags boosts the radar cross section (RCS) and, consequentially, extends the range coverage. Furthermore, an efficient algorithm for tag detection is proposed considering the environmental and hardware impairments that alter the backscattered signal from the tag. In this system, the reader interrogates the reference nodes, i.e. chipless tags, by a frequency-sweep RF signal, and the backscattered signals from the tags are captured and analyzed at the reader for detection, ranging and location estimation. A testbed is implemented with fabricated chipless RFID tags in order to evaluate the validity and accuracy of the proposed self-localization system. Experimental measurements based simulations are conducted in several scenarios in order to evaluate the proposed system. Moreover, an object (the reader) is moved to several locations in an interrogation area of $2.5\times 1.2$ m, and, afterwards, its location is estimated. The averaged measured location error achieved based on the proposed system is approximately 5 cm with a variation of 0.9 cm as a minimum observed error and 7 cm as a maximum error. [ABSTRACT FROM AUTHOR]

Passive chipless tags based on dielectric resonators (DRs) have been proposed for a novel millimeter-wave indoor self-localization system to mark fixed reference nodes. The tags employed in the system provide a unique spectral signature (resonance peaks) with much-increased radar cross section (RCS) by placing several identical DRs at the focal line of a spherical dielectric lens. Resonators of optimized geometry allow a modification of this original DR-lens tag by combining the lens with different size DRs which exhibit resonant frequencies separated by large frequency gaps. For easier manufacturing and testing, a demonstrator tag was designed for a “scaled” frequency range of 5–6 GHz with this configuration and is shown to produce spectral signatures of the monostatic RCS which are uniquely related to the angle of arrival (AoA) of the incident wave from a reader. Simulated signatures of a lens of 120-mm diameter with seven spherical DRs are presented and results are supported by experiment. Correlation processing of the signatures can give a resolution of few degrees in AoA while the bistatic RCS half-power beamwidth is on the order of 20°, and RCS levels of resonant peaks are produced between 0 and −6 dBm2. [ABSTRACT FROM AUTHOR]

Indoor localization based on unsynchronized, low-complexity, passive radio frequency identification (RFID) using the received signal strength indicator (RSSI) has a wide potential for a variety of internet of things (IoTs) applications due to their energy-harvesting capabilities and low complexity. However, conventional RSSI-based algorithms present inaccurate ranging, especially in indoor environments, mainly because of the multipath randomness effect. In this work, we propose RSSI-based localization with low-complexity, passive RFID infrastructure utilizing the potential benefits of large-scale MIMO technology operated in the millimeter-wave band, which offers channel hardening, in order to alleviate the effect of small-scale fading. Particularly, by investigating an indoor environment equipped with extremely simple dielectric resonator (DR) tags, we propose an efficient localization algorithm that enables a smart object equipped with large-scale MIMO exploiting the RSSI measurements obtained from the reference DR tags in order to improve the localization accuracy. In this context, we also derive Cramer–Rao lower bound of the proposed technique. Numerical results evidence the effectiveness of the proposed algorithms considering various arbitrary network topologies, and results are compared with an existing algorithm, where the proposed algorithms not only produce higher localization accuracy but also achieve a greater robustness against inaccuracies in channel modeling. [ABSTRACT FROM AUTHOR]