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ARTI: One New Adaptive Elliptical Weighting Model Combining with the Tikhonov-ℓp-norm for Image Reconstruction

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Broadband Communications, Networks, and Systems (BROADNETS 2021)

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Abstract

To reconstruct the target-induced attenuation image keeping consistent with the observed measurement data, this paper explores the use of a new horizontal distance attenuation-based elliptical weighting model in building an attenuation image, where a horizontal distance attenuation factor and a vertical distance attenuation factor are introduced, respectively, which is able to clear the difference of the voxel weightings perpendicular to the line-of-sight (LOS) direction, as well as the difference of the voxel weightings parallel to the LOS direction. Compared with the existing model, the proposed model can additively reflect the occlusion effect of the radio frequency signal when the target is close to the transceiver nodes. Besides, the Tikhonov-ℓp-norm regularization is incorporated into the image reconstruction, which makes full use of the sparse ability of the ℓp-norm (0 < p < 1) to further reduce the noise interference. The experimental studies on indoor and outdoor scenarios with radio tomographic imaging are presented to validate the effectiveness of the proposed approach.

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Funding

This research was financially supported by National Science Foundation of China (61871176): Research of Abnormal Grain Conditions Detection using Radio Tomographic Imaging based on Channel State Information; National Science Foundation of China (61901159): Research on Beamspace Channel Estimation in Massive MIMO Systems by Fusing Multi-Dimensional Characteristic Information; Applied research plan of key scientific research projects in Henan colleges and Universities (19A510011); Research of Abnormal Grain Conditions Detection Based on Radio Tomographic Imaging based on RSSI; Scientific Research Foundation Natural Science Project In Henan University of Technology (2018RCJH18): Research of Abnormal Grain Conditions Detection using Radio Tomographic Imaging based on Received Signal Strength Information; the Innovative Funds Plan of Henan University of Technology Plan (2020ZKCJ02): Data-Driven Intelligent Monitoring and Traceability Technique for Grain Reserves.

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Authors and Affiliations

  1. Key Laboratory of Grain Information Processing and Control, Ministry of Education, Henan University of Technology, Zhengzhou, 450001, Henan, China

    Chunhua Zhu, Zhen Shi & Weidong Yang

  2. College of Information Science and Engineering, Henan University of Technology, Zhengzhou, 450001, Henan, China

    Chunhua Zhu, Zhen Shi & Weidong Yang

  3. Henan Key Laboratory of Grain Photoelectric Detection and Control, Henan University of Technology, Zhengzhou, 450001, Henan, China

    Chunhua Zhu, Zhen Shi & Weidong Yang

Authors
  1. Chunhua Zhu
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  2. Zhen Shi
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  3. Weidong Yang
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Contributions

Chunhua Zhu and Zhen Shi proposed the original idea and Qinwen Ji carried out the experiment. Chunhua Zhu and Zhen Shi wrote the paper. Zhen Shi supervised and reviewed the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Chunhua Zhu .

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Editors and Affiliations

  1. La Trobe University, Bundoora, VIC, Australia

    Wei Xiang

  2. RMIT University, Melbourne, VIC, Australia

    Fengling Han

  3. La Trobe University, Melbourne, VIC, Australia

    Tran Khoa Phan

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© 2022 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Zhu, C., Shi, Z., Yang, W. (2022). ARTI: One New Adaptive Elliptical Weighting Model Combining with the Tikhonov-ℓp-norm for Image Reconstruction. In: Xiang, W., Han, F., Phan, T.K. (eds) Broadband Communications, Networks, and Systems. BROADNETS 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 413. Springer, Cham. https://doi.org/10.1007/978-3-030-93479-8_14

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  • DOI: https://doi.org/10.1007/978-3-030-93479-8_14

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-93478-1

  • Online ISBN: 978-3-030-93479-8

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