Indoor Large-Scale MIMO-Based RSSI Localization with Low-Complexity RFID Infrastructure

Abstract

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.

Design and Analysis of UHF RFID Tag using MATLAB

“…With the measurements and material being considered, another problem that can arise within the subject would be budgeting. As for the requirement of multiple systems, it would be wiser to design in a smaller size of tags [4]. Since creating algorithms for the program function and antennas can be easy, the decisions that consider the qualities of the hardware will be the main focus if the need for it has arisen.…”

Deep Learning-Based Geomagnetic Navigation Method Integrated with Dead Reckoning

“…Researchers have explored various indoor localization techniques, such as wirelessfidelity (Wi-Fi) [2][3][4][5], Bluetooth (BT) [6,7], radio frequency identification (RFID) [8,9], ultrawideband (UWB) [10][11][12][13], ultrasound [14], and visible light [15,16] technologies. These technologies need some infrastructure facilities, and are susceptible to environmental impacts, such as multipath propagation, signal absorption and building blocking.…”

Filtering Effect on RSSI-Based Indoor Localization Methods

“…Filtering Effect on RSSI-Based Indoor Localization Methods primarily intended for localization purposes (El-Absi et al, 2020).…”

“…Likewise, in emergency situations, stress prevention and management are critical for responders to stay well and to continue to help in the situation. Responders could use indoor localization to help quickly guide them to anyone who is in distress without needing to know the exact layout of the building (El-Absi et al, 2020). The indoor positioning, however, suffers from several challenges that are not present when performing positioning outdoors.…”