scholarly journals Compressive Sensing Based Radio Tomographic Imaging with Spatial Diversity

Sensors ◽  
2019 ◽  
Vol 19 (3) ◽  
pp. 439 ◽  
Author(s):  
Shengxin Xu ◽  
Heng Liu ◽  
Fei Gao ◽  
Zhenghuan Wang
Keyword(s):  
Multipath Fading ◽  
Spatial Diversity ◽  
Tomographic Imaging ◽  
Link Quality ◽  
Small Scale ◽  
Reconstruction Method ◽  
Indoor Environments ◽  
Localization Accuracy ◽  
Measurement Variation ◽  
Radio Tomographic Imaging

Radio tomographic imaging (RTI) has emerged as a promising device-free localization technology for locating the targets with no devices attached. RTI deduces the location information from the reconstructed attenuation image characterizing target-induced spatial loss of radio frequency measurements in the sensing area. In cluttered indoor environments, RF measurements of wireless links are corrupted by multipath effects and thus less robust to achieve a high localization accuracy for RTI. This paper proposes to improve the quality of measurements by using spatial diversity. The key insight is that, with multiple antennae equipped, due to small-scale multipath fading, RF measurement variation of each antenna pair behaves differently. Therefore, spatial diversity can provide more reliable and strong measurements in terms of link quality. Moreover, to estimate the location from the image more precisely and make the image more identifiable, we propose using a new reconstruction regularization linearly combining the sparsity and correlation inherent in the image. The proposed reconstruction method can remarkably reduce the image noise and enhance the imaging accuracy especially in the case of a few available measurements. Indoor experimental results demonstrate that compared to existing RTI improvement methods, our RTI solution can reduce the root-mean-square localization error at least 47% while also improving the imaging performance.

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

Sensors ◽  
2019 ◽  
Vol 19 (5) ◽  
pp. 1034 ◽  
Author(s):  
Chunhua Zhu ◽  
Jiaojiao Wang ◽  
Yue Chen
Keyword(s):  
Imaging System ◽  
Tomographic Imaging ◽  
Measurement Noise ◽  
Moving Target ◽  
Indoor Environments ◽  
Smoothing Filter ◽  
Model Combining ◽  
Proposed Model ◽  
Radio Tomographic Imaging ◽  
Weight Model

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.

scholarly journals Indoor Large-Scale MIMO-Based RSSI Localization with Low-Complexity RFID Infrastructure

Sensors ◽  
2020 ◽  
Vol 20 (14) ◽  
pp. 3933
Author(s):  
Mohammed El-Absi ◽  
Feng Zheng ◽  
Ashraf Abuelhaija ◽  
Ali Al-haj Abbas ◽  
Klaus Solbach ◽  
...  
Keyword(s):  
Radio Frequency Identification ◽  
Large Scale ◽  
Indoor Localization ◽  
Channel Modeling ◽  
Low Complexity ◽  
Small Scale ◽  
Indoor Environments ◽  
Wave Band ◽  
Localization Algorithm ◽  
Localization Accuracy

Indoor localization based on unsynchronized, low-complexity, passive radio frequency identification (RFID) using the received signal strength indicator (RSSI) has a wide potential for a variety of internet of things (IoTs) applications due to their energy-harvesting capabilities and low complexity. However, conventional RSSI-based algorithms present inaccurate ranging, especially in indoor environments, mainly because of the multipath randomness effect. In this work, we propose RSSI-based localization with low-complexity, passive RFID infrastructure utilizing the potential benefits of large-scale MIMO technology operated in the millimeter-wave band, which offers channel hardening, in order to alleviate the effect of small-scale fading. Particularly, by investigating an indoor environment equipped with extremely simple dielectric resonator (DR) tags, we propose an efficient localization algorithm that enables a smart object equipped with large-scale MIMO exploiting the RSSI measurements obtained from the reference DR tags in order to improve the localization accuracy. In this context, we also derive Cramer–Rao lower bound of the proposed technique. Numerical results evidence the effectiveness of the proposed algorithms considering various arbitrary network topologies, and results are compared with an existing algorithm, where the proposed algorithms not only produce higher localization accuracy but also achieve a greater robustness against inaccuracies in channel modeling.

scholarly journals Exploring the Laplace Prior in Radio Tomographic Imaging with Sparse Bayesian Learning towards the Robustness to Multipath Fading

Sensors ◽  
2019 ◽  
Vol 19 (23) ◽  
pp. 5126 ◽  
Author(s):  
Wang ◽  
Guo ◽  
Wang
Keyword(s):  
Wireless Network ◽  
Bayesian Learning ◽  
Signal Reconstruction ◽  
Multipath Fading ◽  
Tomographic Imaging ◽  
Sparse Bayesian Learning ◽  
Attenuation Field ◽  
Radio Tomographic Imaging ◽  
Map Solution ◽  
Localization And Tracking

Radio tomographic imaging (RTI) is a technology for target localization by using radiofrequency (RF) sensors in a wireless network. The change of the attenuation field caused by thetarget 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 RSSvariations are reliable. If the unreliable RSS variations are used for image reconstruction, someartifacts will appear in the shadowing image, which may cause the target’s position being wronglyestimated. Due to the sparse property of the shadowing image, sparse Bayesian learning (SBL) canbe 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 frameworkof SBL. Bayesian modeling, Bayesian inference and the fast algorithm are presented to achieve themaximum-a-posterior (MAP) solution. Finally, imaging, localization and tracking experiments fromthree 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-timeexperiment as well.

scholarly journals Localizing Persons Using Body Area Sensor Network

2012 ◽  
pp. 273-289
Author(s):  
Cheng Guo ◽  
R. Venkatesha Prasad ◽  
Jing Wang ◽  
Vijay Sathyanarayana Rao ◽  
Ignas Niemegeers
Keyword(s):  
Sensor Network ◽  
Signal Strength ◽  
Spatial Diversity ◽  
Location Estimation ◽  
Indoor Environments ◽  
Localization Algorithm ◽  
Body Area ◽  
Localization Accuracy ◽  
Network Location ◽  
Localization Algorithms

Context awareness is an important aspect in many ICT applications. For example, in an intelligent home network, location of the user enables session transfer, lighting, and temperature control, et cetera. In fact, in a body area sensor network (BASN), location estimation of a user helps in realizing realtime monitoring of the person (especially those who require help) for better health supervision. In this chapter the authors first introduce many localization methods and algorithms from the literature in BASNs. They also present classification of these methods. Amongst them, location estimation using signal strength is one of the foremost. In indoor environments, the authors found that the signal strength based localization methods are usually not accurate, since signal strength fluctuates. The fluctuation in signal strength is due to deficient antenna coverage and multi-path interference. Thus, localization algorithms usually fail to achieve good accuracy. The authors propose to solve this problem by combining multiple receivers in a body area sensor network to estimate the location with a higher accuracy. This method mitigates the errors caused by antenna orientations and beam forming properties. The chapter evaluates the performance of the solution with experiments. It is tested with both range-based and range-free localization algorithm that we developed. The chapter shows that with spatial diversity, the localization accuracy is improved compared to using single receiver alone. Moreover, the authors observe that range-based algorithm has a better performance.

scholarly journals Species and characteristics of volatile organic compounds emitted from an auto-repair painting workshop

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
M. Y. Song ◽  
H. Chun
Keyword(s):  
Volatile Organic Compounds ◽  
Organic Compounds ◽  
Large Scale ◽  
Butyl Acetate ◽  
Small Scale ◽  
Indoor Environments ◽  
Voc Emissions ◽  
Volatile Organic ◽  
Before And After ◽  
Adsorption Systems

AbstractVolatile organic compounds (VOCs) are secondary pollutant precursors having adverse impacts on the environment and human health. Although VOC emissions, their sources, and impacts have been investigated, the focus has been on large-scale industrial sources or indoor environments; studies on relatively small-scale enterprises (e.g., auto-repair workshops) are lacking. Here, we performed field VOC measurements for an auto-repair painting facility in Korea and analyzed the characteristics of VOCs emitted from the main painting workshop (top coat). The total VOC concentration was 5069–8058 ppb, and 24–35 species were detected. The VOCs were mainly identified as butyl acetate, toluene, ethylbenzene, and xylene compounds. VOC characteristics differed depending on the paint type. Butyl acetate had the highest concentration in both water- and oil-based paints; however, its concentration and proportion were higher in the former (3256 ppb, 65.5%) than in the latter (2449 ppb, 31.1%). Comparing VOC concentration before and after passing through adsorption systems, concentrations of most VOCs were lower at the outlets than the inlets of the adsorption systems, but were found to be high at the outlets in some workshops. These results provide a theoretical basis for developing effective VOC control systems and managing VOC emissions from auto-repair painting workshops.

scholarly journals Modeling of Non-WSSUS Double-Rayleigh Fading Channels for Vehicular Communications

2017 ◽  
Vol 2017 ◽  
pp. 1-15 ◽  
Author(s):  
Carlos A. Gutiérrez ◽  
J. J. Jaime-Rodríguez ◽  
J. M. Luna-Rivera ◽  
Daniel U. Campos-Delgado ◽  
Javier Vázquez Castillo
Keyword(s):  
Fading Channels ◽  
Communication Systems ◽  
Rayleigh Fading ◽  
Channel Model ◽  
Closed Form Solution ◽  
Multipath Fading ◽  
Rayleigh Distribution ◽  
Rayleigh Fading Channels ◽  
Small Scale ◽  
Time Frequency

This paper deals with the modeling of nonstationary time-frequency (TF) dispersive multipath fading channels for vehicle-to-vehicle (V2V) communication systems. As a main contribution, the paper presents a novel geometry-based statistical channel model that facilitates the analysis of the nonstationarities of V2V fading channels arising at a small-scale level due to the time-varying nature of the propagation delays. This new geometrical channel model has been formulated following the principles of plane wave propagation (PWP) and assuming that the transmitted signal reaches the receiver antenna through double interactions with multiple interfering objects (IOs) randomly located in the propagation area. As a consequence of such interactions, the first-order statistics of the channel model’s envelope are shown to follow a worse-than-Rayleigh distribution; specifically, they follow a double-Rayleigh distribution. General expressions are derived for the envelope and phase distributions, four-dimensional (4D) TF correlation function (TF-CF), and TF-dependent delay and Doppler profiles of the proposed channel model. Such expressions are valid regardless of the underlying geometry of the propagation area. Furthermore, a closed-form solution of the 4D TF-CF is presented for the particular case of the geometrical two-ring scattering model. The obtained results provide new theoretical insights into the correlation and spectral properties of small-scale nonstationary V2V double-Rayleigh fading channels.

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

2017 ◽  
Vol 2017 ◽  
pp. 1-10
Author(s):  
Fei Gao ◽  
Cheng Sun ◽  
Heng Liu ◽  
Jianping An ◽  
Shengxin Xu
Keyword(s):  
Imaging System ◽  
Gaussian Mixture ◽  
Tomographic Imaging ◽  
System Model ◽  
Wireless Sensor ◽  
Target Image ◽  
Estimation Model ◽  
Rti Model ◽  
Restoration Method ◽  
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.

Reducing Tracking Error in RFID Real-Time Localization Systems Using Kalman Filters

2014 ◽  
Vol 5 (3) ◽  
pp. 1-24
Author(s):  
Benjamin Sanda ◽  
Ikhlas Abdel-Qader ◽  
Abiola Akanmu
Keyword(s):  
Real Time ◽  
Radio Frequency Identification ◽  
Kalman Filters ◽  
Tracking Error ◽  
Time Of Arrival ◽  
Indoor Environments ◽  
Real World Data ◽  
Localization Accuracy ◽  
Accurate Localization ◽  
Localization Systems

The use of Radio Frequency Identification (RFID) has become widespread in industry as a means to quickly and wirelessly identify and track packages and equipment. Now there is a commercial interest in using RFID to provide real-time localization. Efforts to use RFID technology in this way experience localization errors due to noise and multipath effects inherent to these environments. This paper presents the use of both linear Kalman filters and non-linear Unscented Kalman filters to reduce the error rate inherent to real-time RFID localization systems and provide more accurate localization results in indoor environments. A commercial RFID localization system designed for use by the construction industry is used in this work, and a filtering model based on 3rd order motion is developed. The filtering model is tested with real-world data and shown to provide an increase in localization accuracy when applied to both raw time of arrival measurements as well as final localization results.

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