Backprojection and Integration for the Multi-Scale Spatial Model in Radio Tomographic Imaging

Tan, Jiaju; Zhao, Xin; Yang, Liwei; Guo, Xuemei; Wang, Guoli

doi:10.1109/cyber.2018.8688136

Cited by 3 publications

(3 citation statements)

References 16 publications

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“…The RSS measurement (1) of link i can be formulated as:yi=∑j=1NΦi,jxj+ei=ϕi⊤bold-italicx+ei, so that this reconstruction issue of x from the measurement Equation (1) can be treated as a regression problem, where {false(ϕi⊤,yifalse)}i=1M can be regarded as the training data. Then, by transforming this regression problem into a support vector regression optimization [27], the optimal x can be acquired as:bold-italicx=∑i=1Mziϕi=Φ⊤bold-italicz, where bold-italicz=false[z1,⋯,zMfalse]⊤ is the weight that should be determined in BRTI [17].…”

Section: Related Workmentioning

confidence: 99%

“…Meanwhile, the calculation for reconstructing the high-dimensional image is so high that the real-time DFL performance is limited. To overcome this time-consuming problem of solving the under-determined equations in RTI directly, the Back-projection RTI (BRTI) has been proposed to realize the lightweight imaging for DFL applications [17,18]. It is verified that the shadow fading can be regarded as the weighted sum of RF link attenuation.…”

Section: Introductionmentioning

confidence: 99%

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Efficient Recognition of Informative Measurement in the RF-Based Device-Free Localization

Tan

Guo

Zhao

et al. 2019

Sensors

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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.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Efficient Recognition of Informative Measurement in the RF-Based Device-Free Localization

Tan

Guo

Zhao

et al. 2019

Sensors

Self Cite

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show abstract

“…However, there are still some noisy block artifacts in the shadowing image which may render the target’s position wrongly estimated. Based on the multi scale model [39], a back-projection and integrated imaging is used to reduce the multipath fading in [40]. Nevertheless, one drawback which hinders this approach to the practical application is that the parameters in this approach are quite sensitive to the scenario.…”

Section: Introductionmentioning

confidence: 99%

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

Wang

Guo

2019

Sensors

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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.

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

Tan

Zhao²,

Guo

et al. 2022

Computer Communications

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Backprojection and Integration for the Multi-Scale Spatial Model in Radio Tomographic Imaging

Cited by 3 publications

References 16 publications

Efficient Recognition of Informative Measurement in the RF-Based Device-Free Localization

Efficient Recognition of Informative Measurement in the RF-Based Device-Free Localization

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

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

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