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Sense-Through-Foliage Target Detection Based on Stacked Autoencoder and UWB Radar Sensor Networks

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Communications, Signal Processing, and Systems (CSPS 2021)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 878))

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Abstract

In this paper, we proposed a stacked autoencoder (SAE)-based approach to ultra wide band (UWB) radar for sense-through-foliage target detection. As one of the widely used deep learning structures, SAE could learn representations of data with multiple levels of abstraction automatically. Processing the poor signal collections, in some positions, the SAE-based target detection approach performed well. While in other positions, a single radar target detection performed under satisfaction, RAKE structure was applied in radar sensor networks with maximum ratio combing and equal combine to integrate radar echoes from different radar cluster-members. The experimental results showed that the RAKE-SAE-based method could qualify the mission of target detection.

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Author information

Authors and Affiliations

  1. The University of Texas at Arlington, Arlington, USA

    Chengchen Mao & Qilian Liang

Authors
  1. Chengchen Mao
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  2. Qilian Liang
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Corresponding author

Correspondence to Chengchen Mao .

Editor information

Editors and Affiliations

  1. Department of Electrical Engineering, University of Texas at Arlington, Arlington, TX, USA

    Qilian Liang

  2. Tianjin Normal University, Tianjin, China

    Wei Wang

  3. Dalian University of Technology, Dalian, China

    Xin Liu

  4. School of Information Science and Technology, Dalian Maritime University, Dalian, China

    Zhenyu Na

  5. College of Electronic and Communication Engineering, Tianjin Normal University, Tianjin, China

    Baoju Zhang

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© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Mao, C., Liang, Q. (2022). Sense-Through-Foliage Target Detection Based on Stacked Autoencoder and UWB Radar Sensor Networks. In: Liang, Q., Wang, W., Liu, X., Na, Z., Zhang, B. (eds) Communications, Signal Processing, and Systems. CSPS 2021. Lecture Notes in Electrical Engineering, vol 878. Springer, Singapore. https://doi.org/10.1007/978-981-19-0390-8_48

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  • DOI: https://doi.org/10.1007/978-981-19-0390-8_48

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-19-0389-2

  • Online ISBN: 978-981-19-0390-8

  • eBook Packages: EngineeringEngineering (R0)

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