A review on uncertainty quantification of shadowing reconstruction and signal measurements in Radio Tomographic Imaging

Abstract

Radio Tomographic Imaging (RTI) is a novel technology to reconstruct the target-induced shadowing effect of signal propagation in the Radio-Frequency (RF) sensing network, through the Received Signal Strength (RSS) measurements in the RF propagation links. As a novel wireless-signal reconstruction technology, RTI is widely utilized in the Device-Free Localization (DFL) and Location-Based Services (LBS) applications. However, the multipath interference in RF sensing network often induces serious uncertainty in RTI system, including the RSS measurement noise and the RTI reconstruction degradation. Addressing this problem, as the RTI system is to reconstruct the probability image of shadowing by using the RSS measurements, from the perspective of probabilistic modeling, the RTI reconstruction issue is modeled as the Maximum a Posterior Probability (MAP) of the reconstructed shadowing image from the RSS measurements. Then lots of RTI reconstruction methods for uncertainty quantification, have been developed to improve the RSS measurement accuracy and the RTI reconstruction quality. Therefore, for the first time, this article conducts a comprehensive survey of the recent RTI methods from the viewpoint of uncertainty quantification. At first, the uncertainty quantification in these RTI methods of the shadowing image and RSS measurements are united into the Epistemic uncertainty and Aleatoric uncertainty respectively. Besides, these methods are further classified into different categories based on their technical commonalities. Moreover, the presenting challenges of uncertainty quantification in the actual RTI applications are also discussed, and based on that, the potential development directions of these methods for improving the RSS measurement accuracy and RTI reconstruction performance in future are reasonably predicted.