EM Model-Based Device-Free Localization of Multiple Bodies

Author:

Rampa VittorioORCID,Nicoli MonicaORCID,Manno Chiara,Savazzi StefanoORCID

Abstract

In this paper, we discuss the problem of device-free localization and tracking, considering multiple bodies moving inside an area monitored by a wireless network. The presence and motion of non-instrumented subjects leave a specific footprint on the received Radio-Frequency (RF) signals by affecting the Received Signal Strength (RSS) in a way that strongly depends on people location. The paper targets specifically the modelling of the effects on the electromagnetic (EM) field, and the related inference methods. A multiple-body diffraction model is exploited to predict the impact of these bodies on the RSS field, i.e., the multi-body-induced shadowing, in the form of an extra attenuation w.r.t. the reference scenario where no targets are inside the monitored area. Unlike almost all methods available in the literature, that assume multi-body-induced shadowing to sum linearly with the number of people co-present in the monitored area, the proposed model describes also the EM effects caused by their mutual interactions. As a relevant case study, the proposed EM model is exploited to predict and evaluate the effects due to two co-located bodies inside the monitored area. The proposed real-time localization and tracking method, exploiting both average and deviation of the RSS perturbations due to the two subjects, is compared against others techniques available in the literature. Finally, some results, based on experimental RF data collected in a representative indoor environment, are presented and discussed.

Funder

Ministero dell’Istruzione, dell’Università e della Ricerca

Publisher

MDPI AG

Subject

Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry

Cited by 8 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. An EM Body Model for Device-Free Localization with Multiple Antenna Receivers: A First Study;2023 IEEE-APS Topical Conference on Antennas and Propagation in Wireless Communications (APWC);2023-10-09

2. Modelling of the floor effects in device-free radio localization applications;2023 17th European Conference on Antennas and Propagation (EuCAP);2023-03-26

3. Passive infrared sensor dataset and deep learning models for device-free indoor localization and tracking;Pervasive and Mobile Computing;2023-01

4. Deep CNN-LSTM Network for Indoor Location Estimation Using Analog Signals of Passive Infrared Sensors;IEEE Internet of Things Journal;2022-11-15

5. Electromagnetic Models for Device-Free Radio Localization with Antenna Arrays;2022 IEEE-APS Topical Conference on Antennas and Propagation in Wireless Communications (APWC);2022-09-05

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