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.
Highlights
Recent research activities have shown that electromagnetic (EM) fields used for data communication can be exploited as signals of opportunity for device-free environmental radio vision [1,2,3]
Each anchor node is equipped with a NXP JN5148 Systemon-chip (SoC) wireless micro-controller, that enables time-slotted transmissions within the 2.4 GHz band according to the IEEE 802.15.4 standard, and other ancillary interface components [47]
The Received Signal Strength (RSS) dynamic range of the SoC built-in receiver is equal to 75 dB with a minimum sensitivity of −95 dBm while the transmit power is about 0 dBm
Summary
Recent research activities have shown that electromagnetic (EM) fields used for data communication can be exploited as signals of opportunity for device-free (i.e., passive) environmental radio vision [1,2,3]. The original contributions of this paper are as follows: (i) a novel dualtarget EM model that accounts for small voluntary/involuntary movements of two human bodies and underpins a Bayesian framework for full dual-target localization and tracking; (ii) a procedure for the calibration of the model parameters based on field data, and the evaluation of the dual-target perturbation maps (RSS mean and standard deviations); (iii) a link selection procedure for dropping out unreliable link measurements; (iv) the design of a new family of non-Bayesian (JML, SC and ML-RTI) and Bayesian (PF) methods that exploit the new model for multi-target localization and take advantage of both average and fluctuation of RSS readings to increase the DFL accuracy; and (v) the validation and comparison of the proposed model-based methods in real indoor scenarios.
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