Radio Tomographic Imaging System Research Articles

MEASUREMENT OF RICE MOISTURE CONTENT BASED ON QUANTITATIVE ANALYSIS FROM RADIO TOMOGRAPHY IMAGES

Inefficient storage of paddy and rice grains can lead to grain deterioration, resulting in post-harvest losses ranging from 10% to 30%. The quality of grains cannot be improved throughout the storage period. Therefore, following the mechanisation of agricultural industries, air dryers have been developed to control the crops’ moisture level by blowing ambient or heated air into the silo to improve the aeration and allow the grains to be preserved with minimal loss of quality until the appropriate time for managing and marketing processes. However, the conventional sampling method used to measure the moisture level is inefficient because it is very localised and only represents part of the moisture distribution inside the bulk grains. Additionally, incorporating advanced technologies can be a significant cost limitation for small-scale industries. Thus, to address the issue, this research study developed a radio tomographic imaging (RTI) system in a silo-scale prototype using 20 sensor nodes operating at 2.4 GHz to localise and monitor the moisture level constructively. The RTI system reconstructs the cross-sectional images across the rice silo by measuring radio frequency attenuation, in terms of received signal strength (RSS) quality, caused by the rice moisture phantoms within the wireless sensor network (WSN) area. A total of five phantoms’ profiles having a percentage of moisture content (MC) of 15%, 20% and 25% were reconstructed using four image reconstruction algorithms, Linear Back Projection (LBP), Filtered Back Projection (FBP), Newton’s One-step Error Reconstruction (NOSER) and Tikhonov Regularisation.

Second-Order Fused Lasso Algorithm for Radio Tomographic Imaging

Radio tomographic imaging (RTI) is one of the device-free localization (DFL) approaches used to identify obstacles in a wireless network by using the attenuation information of radio waves. The RTI system’s fusion centre collects the received signal strength (RSS) data from all nodes and uses batch estimation to find the spatial loss field (SLF) due to obstacles. The surrounding small noisy pixels in the SLF vector are eliminated by the least absolute shrinkage and selection operator (lasso), which results in improved sparsity in SLF. First-order fused lasso (FL)-based RTI techniques are used for simultaneous improvements in sparsity and structural details of the SLF. However, first-order methods have slower convergence than the second-order methods. Also, this batch FL-based SLF estimation results in high memory requirements. In this letter, a novel second-order fast FL algorithm is proposed to handle such bottlenecks. This algorithm uses a time- and norm-weighted fused lasso (TNWFL) strategy for updating the weights of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">l</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -norm through the use of second-order parameters. The effectiveness of the TNWFL estimator is verified through the simulations.

Sparsity promoting decentralized learning strategies for radio tomographic imaging using consensus based ADMM approach

Radio tomographic imaging (RTI) has wide applications in the detection and tracking of objects that do not require any sensor to be attached to the object. Consequently, it leads to device-free localization (DFL). RTI uses received signal strength (RSS) at different sensor nodes for imaging purposes. The attenuation maps, known as spatial loss fields (SLFs), measure the power loss at each pixel in the wireless sensor network (WSN) of interest. These SLFs help us to detect obstacles and aid in the imaging of objects. The centralized RTI system requires the information of all sensor nodes available at the fusion centre (FC), which in turn increases the communication overhead. Furthermore, the failure of links may lead to improper imaging in the RTI system. Hence, a distributed approach for the RTI system resolves such problems. In this paper, a consensus-based distributed strategy is used for distributed estimation of the SLF. The major contribution of this work is to propose a fully decentralized RTI system by using a consensus-based alternating direction method of multipliers (ADMM) algorithm to alleviate the practical issues with centralized and distributed incremental strategies. We proposed distributed consensus ADMM (DCADMM-RTI) and distributed sparse consensus ADMM (DSCADMM-RTI) for the RTI system to properly localize targets in a distributed fashion. Furthermore, the effect of quantization noise is verified by using the distributed consensus algorithms while sharing the quantized data among the neighbourhoods.

Exploring the spatial correlation in radio tomographic imaging by block‐structured sparse Bayesian learning

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.

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

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.

Exploiting Ultra-Wideband Channel Impulse Responses for Device-Free Localization.

In radio-frequency (RF)-based device-free localization (DFL), the number of sensors acting as RF transmitters and receivers is crucial for accuracy and system costs. Two promising approaches for DFL have been identified in the past: radio tomographic imaging (RTI) and multi-static radar (MSR). RTI in its basic version requires many sensors for high accuracy, which increases the cost. In this paper, we show how RTI benefits from multipath propagation. By evaluating the direct and echo paths, we increase the coverage of the target area, and by utilizing UWB signals, the RTI system is less susceptible to multipath propagation. MSR maps reflections that occur within the target area to reflectors such as persons or other objects. MSR does not require that the person is located near a signal path. Both suggested methods exploit ultra-wideband (UWB) channel impulse response (CIR) measurements. CIR measurements and the modeling of multipath effects either increase the accuracy or reduce the required number of sensors for localization with RTI. We created a test setup and measure UWB CIRs at different positions with a commercially available off-the-shelf UWB radio chip, the Decawave DW1000. We compare the localization results of RTI, multipath-assisted (MA)-RTI, and MSR and investigate a combined approach. We show that RTI is improved by the analysis of multipath propagation; furthermore, MA-RTI results in a better performance compared to MSR: with 50% of all cases, the localization error is better than 0.82 m and in 80% of all cases 1.34 m. The combined approach results in the best localization result with 0.64 m in 50% of all cases.

Sparsity-enabled radio tomographic imaging using quantized received signal strength observations

Detection and tracking of objects in wireless sensor networks (WSN) is one of the active research areas in the signal processing community, where device-free localization (DFL) techniques are the potential contributors. Radio tomographic imaging (RTI) is one such DFL technique where the loss field surrounded by any obstacle can be visualized with the help of the received signal strength (RSS) of various transceiver nodes. Due to strict communication constraints and the necessity of low-complexity sensors, we are motivated towards quantized RSS (q-RSS) observations in the RTI system. The RSS data is digitized before sending it to the fusion center (FC). Therefore, uncertainty due to RSS quantization error leads to momentous performance degradation of the RTI system. The proposed support vector regression (SVR) technique reduces this uncertainty. The ϵ-SVR and ν-SVR techniques are affected by noise levels, and their performance is enhanced by the use of fused l2-SVR (F-l2-SVR). However, these estimators have higher computational complexity due to the large number of support vectors. Hence, a robust sparsity-based estimator using the linear programming SVR (LP-SVR) is proposed to reduce the computational complexity. Further, a novel fused lasso-based l1-SVR (FL-l1-SVR) estimator is proposed that provides estimated spatial loss field (SLF) as close as to the true SLF. Finally, the performance metrics and the reduced number of support vectors lead to the efficacy of the proposed FL-l1-SVR estimator over other estimators under q-RSS observations in the RTI system.

Attention Based Bidirectional Convolutional LSTM for High-Resolution Radio Tomographic Imaging

Radio tomographic imaging (RTI) is a technique for imaging the environment by using the received signal strength (RSS) measurements from a wireless sensor network. This brief considers the data fusion for a sequence of RSS measurements, and proposes an attention based bidirectional convolutional long short-term memory (LSTM) based deep learning method to achieve the high-resolution RTI. A time sequence of tomographic images of the dynamic environment can be obtained efficiently by employing the developed RTI system. The effectiveness of the presented method is demonstrated by the simulation examples.

ARTI (Adaptive Radio Tomographic Imaging): One New Adaptive Elliptical Weighting Model Combining with Tracking Estimates.

Imaging and tracking performance suffers from the mismatch between the model and the measurements in an adaptive radio tomographic imaging system. In this paper, a model-based approach is reviewed and a new adaptive elliptical weighting model is proposed, in which the coverage of ellipse and the voxels weightings can adaptively match the actual environments, and the Savitzky–Golay smoothing filter is presented to eliminate the influence of measurement noise and multipath interference. In our proposed model, the optimal coverage of ellipse and weightings can be obtained from voxel weightings distribution inside the ellipse and pseudo-position area and trailing phenomenon. Finally, the development efforts are evaluated and validated with real experiments conducted in indoor environments for a moving target. The results have shown that the proposed algorithm can improve the accuracy of image and location estimates compared with the normalized weight model and the const-eccentricity weight model.

ARTI: An Adaptive Radio Tomographic Imaging System

Radio tomographic imaging systems use received signal strength measurements between static wireless sensors to image the changes in the radio propagation environment in the area of the sensors, which can be used to localize a person causing the change. To date, spatial models used for such systems are set a priori and do not change. Imaging and tracking performance suffers because of the mismatch between the model and the measurements. Collecting labeled training data requires intensive effort, and the data degrade quickly as the environment changes. This paper provides a means for a radio tomographic imaging system to bootstrap to improve its spatial models using unlabeled data, iteratively improving itself over time. A collection of tracking filters are presented to improve the accuracy of image and coordinate estimates. This paper presents an online method to use these estimates to instantaneously update spatial model parameters. Further, a smoothing method is presented to fine-tune the model with a given finite latency. The development efforts are evaluated using simulations and validated with real-world experiments conducted in three different environments. With respect to another state-of-the-art radio tomographic imaging system, the results suggest that the presented system increases the median tracking accuracy by twofold in the most challenging environment and by threefold when the model parameters are trained using the smoothing method.

On-Wall, Wide Bandwidth E-Shaped Patch Antenna for Improved Whole-Home Radio Tomography

Tagless identification and tracking with through-wall received signal strength-based radio tomographic imaging (RTI) allows emergency responders to learn where people are inside of a building before entering the building. Use of directional antennas in RTI nodes focuses RF power along the link line, improving system performance. However, antennas placed on a building’s exterior wall can be detuned by their close proximity to the dielectric, thus sending power across wider angles and resulting in less accurate imaging. In this paper, we improve through-wall RTI by using an E-shaped patch antenna we design to be mounted to an exterior wall. Along with its directionality, the E-shaped patch antenna is designed to avoid impedance mismatches when brought into close proximity of a dielectric material, thus increasing radiation through the exterior wall and along the link line. From our experiments, we demonstrate that the E-shaped patch antenna can reduce the median root mean square localization error by up to 43% when compared to microstrip patch and dipole antennas. For equal error performance, the E-shaped patch antenna allows an RTI system to reduce power and bandwidth usage by using fewer nodes and measuring on fewer channels.

An Image Restoration Method Using Matrix Transform and Gaussian Mixture Model for Radio Tomographic Imaging

Radio Tomographic Imaging (RTI) is an attractive technique for imaging the nonmetallic targets within wireless sensor network. RTI has been used in many challenging environments and situations. Due to the accuracy of Radio Tomographic Imaging system model and the interference between the wireless signals of sensors, the image obtained from the RTI system is a degraded target image, which cannot offer sufficient details to distinguish different targets. In this paper, we treat the RTI system as an image degraded process, and we propose an estimation model based on mixture Gaussian distribution to derive the degradation function from the shadowing-based RTI model. Then we use this degradation function to recover an original image by a method called constrained least squares filtering. So far, many imaging models have been proposed for localization; however, they do not have a satisfied imaging accuracy. Simulated and experimental results show that the imaging accuracy of our proposed method is improved, and the proposed method can be used in the real-time circumstances.

Simulating a Multi-Vehicle Traffic Sensing System Based on Radio Tomographic Imaging

One of the most recently developed wireless technologies is Radio Tomographic Imaging (RTI). RTI employs a wireless sensor network that produces images of the change in the electromagnetic field of a monitored area using Received Signal Strength (RSS) measurements. This allows the tracking of device-free objects such as humans and cars. This paper is the first to propose and validate a simulation model that simulates RSS measurements for arbitrary RTI networks, based on the ZigBee communication protocol. The simulation model allows the specification of an RTI network from the ground up, including node positions, network size and geometry and RSS measurement processing. Furthermore, this paper demonstrates the implementation of the simulation model to an enhancement of a recently proposed RTI system, which acts as a roadside surveillance system. The enhancement includes three newly proposed techniques, namely a new weight matrix calculation method, a new node spacing setup and a new vehicle detection method. The simulation results indicate that it is possible to detect both one or two family sized cars simultaneously. Using techniques that reduce RSS variance due to multipath effects and the newly proposed methods, simulated vehicle detection performance is demonstrated to be between 95% and 100%.

&lt;italic&gt;RTI Goes Wild&lt;/italic&gt;: Radio Tomographic Imaging for Outdoor People Detection and Localization

In recent years, Radio frequency (RF) sensor networks have been used to localize people indoor without requiring them to wear invasive electronic devices. These wireless mesh networks, formed by low-power radio transceivers, continuously measure the received signal strength (RSS) of the links. Radio Tomographic Imaging (RTI) is a technique that generates, starting from these RSS measurements, 2D images of the change in the electromagnetic field inside the area covered by the radio transceivers to spot the presence and movements of animates (e.g., people, large animals) or large metallic objects (e.g., cars). Here, we present a RTI system for localizing and tracking people outdoors. Differently than in indoor environments where the RSS does not change significantly with time unless people are found in the monitored area, the outdoor RSS signal is time-variant, e.g., due to rainfall or wind-driven foliage. We present a novel outdoor RTI method that, despite the nonstationary noise introduced in the RSS data by the environment, achieves high localization accuracy and dramatically reduces the energy consumption of the sensing units. Experimental results demonstrate that the system accurately detects and tracks a person in real-time in a large forested area under varying environmental conditions, significantly reducing false positives, localization error and energy consumption compared to state-of-the-art RTI methods.

Accuracy vs. Resolution in Radio Tomography

Radio tomographic imaging (RTI) has recently been proposed for tracking object location via radio waves without requiring the objects to transmit or receive radio signals. The position is extracted by inferring which voxels are obstructing the various radio links in a dense wireless sensor network. This paper derives an analytic expression for the image accuracy (error per voxel) for each of 5 published RTI system models. The formulae show the effects of weight model choice, voxel size, number of sensors, and degree of regularization on the Cramer-Rao Lower Bound (CRLB). This enables analysis of the tradeoffs between these parameters in system design, particularly between image accuracy and image resolution. The theoretical results agree well with simulations, and the new theory is used to interpret an experimental scenario.

Radio Tomographic Imaging with Wireless Networks

Radio Tomographic Imaging (RTI) is an emerging technology for imaging the attenuation caused by physical objects in wireless networks. This paper presents a linear model for using received signal strength (RSS) measurements to obtain images of moving objects. Noise models are investigated based on real measurements of a deployed RTI system. Mean-squared error (MSE) bounds on image accuracy are derived, which are used to calculate the accuracy of an RTI system for a given node geometry. The ill-posedness of RTI is discussed, and Tikhonov regularization is used to derive an image estimator. Experimental results of an RTI experiment with 28 nodes deployed around a 441 square foot area are presented.