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Radio Tomographic Imaging (RTI) is a low‐cost computational imaging method realised by the Radio Frequency (RF) signal sensing. The target‐induced shadowing effect in the RF sensing network is reconstructed as a probability image to estimate the target's position. Then, the RTI‐based Device‐free Localization (DFL) is becoming a promising research topic in the Location‐based Services applications by the Internet of Things (IoT). However, the multipath interference in the RF sensing network often induces the imaging degradation and decreases the DFL accuracy. To deal with the multipath‐induced imaging degradation, considering that the target's shadowing occupies a small spatial range in the RF network and expresses some spatial structure, this article explores the spatial correlation in the target's shadowing. Then, a new RTI reconstruction method based on the Structured Sparse Bayesian Learning is proposed to model the spatial correlation implied in the sparse target's shadowing image. Further, the localisation experiments in actual scenes are conducted to validate the utilisation of the spatial correlation in target's shadowing is able to improve the imaging quality of the RTI system by enhancing the robustness towards the multipath‐induced imaging degradation. [ABSTRACT FROM AUTHOR]

Radio tomographic imaging (RTI) is a technology to locate the target by using an attenuation image. The attenuation image can be reconstructed from the shadowing received signal strength (RSS) measurements in a wireless network. However, due to multi-path interference and the limited measuring capability in hardware, not all the RSS measurements in a wireless network are informative shadowing measurements. Some measurements may increase or remain unchanged when the target shadows some links in the wireless network, which means that this part of RSS measurements is polluted. If the polluted RSS measurements are used to reconstruct the attenuation image, the image quality and the localization accuracy will deteriorate. To alleviate the effect of polluted RSS measurements, a dual-RTI approach with shadowing-measurement awareness is developed in this article. The proposed dual-RTI consists of two steps: 1) signal reconstruction step in dual space and 2) a backprojection step to obtain the attenuation image. The reconstruction in dual space can select the informative shadowing measurements automatically and allocate superior weights to them. Then, the backprojection step just utilizes the measurement vectors from the shadowing links to obtain the attenuation image. These two steps allow the dual-RTI to achieve better image quality and localization accuracy. Furthermore, the dual-RTI reduces the time consumption of the localization system, which facilitates the practical engineering applications. Finally, experiments from three different scenarios are conducted to validate the advantages of the dual-RTI approach. [ABSTRACT FROM AUTHOR]

This work explores the novel use of Bayesian compressive sensing (BCS) in radio tomographic imaging (RTI), which aims at addressing the performance degradation of shadow fade imaging due to multipath interferences, through the sophisticated efforts on enhancing BCS with the capability of heterogeneous-noise-variance learning. Our contribution is twofold. Firstly, we incorporate a hierarchical model of heterogeneous noise variances into sparse Bayesian learning, which can contribute to the enhancement of BCS in terms of noise-variance awareness. Then, under our enhanced BCS (namely heterogeneous BCS) framework, we develop two learning algorithms for the RTI reconstruction. Theoretical analysis will show the potential advantages of using our heterogeneous BCS in mitigating the effect of multipath interferences, as well as in improving the RTI performance with our learning algorithms. Finally, the experimental results in the context of device-free localization and tracking are reported to demonstrate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

One common challenge in device-free localization (DFL) with radio frequency sensor networks is that the variations of received signal strength (RSS) become less sensitive to shadow fading and the performance will be degenerated in a cluttered environment. To address this problem, this paper develops a hierarchical model that contributes to refining the description granularity of RSS variations and enhancing the sensitivity of RSS variations to shadow fading. The novelty is twofold. First, the Exponential-Rayleigh model and the diffraction-based model are integrated to form a two degree-of-freedom parametrization that can capture not only the target-induced multipath interferences but also the diffraction fading contributions. Second, the link dependent characterizations of RSS variations are explored by parameterizing the model coefficients in terms of fade level. The proposed model is experimentally validated in the context of radio tomographic imaging (RTI). [ABSTRACT FROM AUTHOR]

To reconstruct the target-induced attenuation image, the existing radio tomographic imaging techniques often search the solution in the attenuation signal space and regularize the solution to be consistent with explicitly pre-defined prior knowledge of the attenuation signal. However, the performance will inevitably deteriorate when the prior knowledge fails to be consistent with the signal, especially in complicated scenarios. To address this issue, this paper explores the use of generative model in building an attenuation image. The logic behind of using generative model technique here is to learn the inherent latent structure of the attenuation signal from the data itself rather than its prior knowledge. Specifically, an estimate of the unknown signal will be learnt by means of an untrained generative model and it will be kept consistent with the observed measurement data. In the context of device-free localization of interest here, the L1 regularization is incorporated into the learning process, which will promote the sparsity of the solution. The experimental studies on device-free localization with radio tomographic imaging are presented to validate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Radio tomographic imaging (RTI) is a technology for target localization by using radio frequency (RF) sensors in a wireless network. The change of the attenuation field caused by the target is represented by a shadowing image, which is then used to estimate the target's position. The shadowing image can be reconstructed from the variation of the received signal strength (RSS) in the wireless network. However, due to the interference from multi-path fading, not all the RSS variations are reliable. If the unreliable RSS variations are used for image reconstruction, some artifacts will appear in the shadowing image, which may cause the target's position being wrongly estimated. Due to the sparse property of the shadowing image, sparse Bayesian learning (SBL) can be employed for signal reconstruction. Aiming at enhancing the robustness to multipath fading, this paper explores the Laplace prior to characterize the shadowing image under the framework of SBL. Bayesian modeling, Bayesian inference and the fast algorithm are presented to achieve the maximum-a-posterior (MAP) solution. Finally, imaging, localization and tracking experiments from three different scenarios are conducted to validate the robustness to multipath fading. Meanwhile, the improved computational efficiency of using Laplace prior is validated in the localization-time experiment as well. [ABSTRACT FROM AUTHOR]

Device-Free Localization (DFL) based on the Radio Frequency (RF) is an emerging wireless sensing technology to perceive the position information of the target. To realize the real-time DFL with lower power, Back-projection Radio Tomographic Imaging (BRTI) has been used as a lightweight method to achieve the goal. However, the multipath noise in the RF sensing network may interfere with the measurement and the BRTI reconstruction performance. To resist the multipath interference in the observed data, it is necessary to recognize the informative RF link measurements that are truly affected by the target appearance. However, the existing methods based on the RF link state analysis are limited by the complex distribution of the RF link state and the high time complexity. In this paper, to enhance the performance of RF link state analysis, the RF link state analysis is transformed into a decomposition problem of the RF link state matrix, and an efficient RF link recognition method based on the low-rank and sparse decomposition is proposed to sense the spatiotemporal variation of the RF link state and accurately figure out the target-affected RF links. From the experimental results, the RF links recognized by the proposed method effectively reflect the target-induced RSS measurement variation with less time. Besides, the proposed method by recognizing the informative measurement is helpful to improve the accuracy of BRTI and enhance the efficiency in actual DFL applications. [ABSTRACT FROM AUTHOR]

Abstract This paper concerns the issue of monitoring elderly behavior in the context of ambient assisted living (AAL). Under the framework of online daily habit modeling (ODHM), we employ the emergent representation for activities of daily living (ADLs) with position-based stigmergy, and then combine it with convolution neural networks (CNNs) to accomplish the tasks of recognizing ADLs. In addition, we propose a new paradigm of activity summarization with the robustness to break interruptions. Radio tomographic imaging (RTI) is promoted as a simple yet flexible way of facilitating the required position-based stigmergy. Such position-based AAL systems can benefit the advantages of having no need any sophisticated domain models in analyzing and understanding ADLs while no burden training is involved in ODHM. Moreover, the emergent based data aggregation and deep learning of CNN together allow the recognition of ADLs at a fine-grained level, which contributes to the performance improvement of ODHM. Experimental results demonstrate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]