Deep Learning Approach for Target Locating in Through-the-Wall Radar under Electromagnetic Complex Wall

Ghorbani, Fardin; Soleimani, Hossein

doi:10.48550/arxiv.2102.07990

“…AI and ML techniques can also play a significant role in the performance improvements of TTW systems [161]. Examples include learning better approaches for removal and/or mitigation of wall effects and suppression of ghost targets via generative adversarial networks and denoising networks, as well as dynamic transmit waveform adaptation for improving the signal-to-clutter ratio and switching between different frequency bands to exploit more favourable propagation conditions.…”

Section: Advances In Science and Technology To Meet Challengesmentioning

confidence: 99%

Roadmap on signal processing for next generation measurement systems

Iakovidis

¹

,

Ooi

²

,

Kuang

³

et al. 2021

Meas. Sci. Technol.

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Signal processing is a fundamental component of almost any sensor-enabled system, with a wide range of applications across different scientific disciplines. Time series data, images, and video sequences comprise representative forms of signals that can be enhanced and analysed for information extraction and quantification. The recent advances in artificial intelligence and machine learning are shifting the research attention towards intelligent, data-driven, signal processing. This roadmap presents a critical overview of the state-of-the-art methods and applications aiming to highlight future challenges and research opportunities towards next generation measurement systems. It covers a broad spectrum of topics ranging from basic to industrial research, organized in concise thematic sections that reflect the trends and the impacts of current and future developments per research field. Furthermore, it offers guidance to researchers and funding agencies in identifying new prospects.

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“…Recently, DNN algorithms with their potential characteristics for solving new problems in communications have been widely used [27]- [32]. In 2019, a MS design method called REACTIVE developed by T. Cui et al, has been proposed to detect the internal rules between unit cell structures and their electromagnetic (EM) properties [33].…”

Section: Introductionmentioning

confidence: 99%

Multifunctional Automatic Metasurface Design Using Deep Learning Approach

Mahdi¹,

Ghorbani²,

Beyraghi³

et al. 2022

Preprint

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<p>This paper presents a new efficient and robust method for the automatic design of Metasurface (MS) by using a Deep Neural Network (DNN), which has less complexity and design time compared to conventional MS design methods. The main contribution of the present method is its ability the design reflective/transmissive MS for wave manipulation in a wide range of frequencies (12 GHz - 18 GHz). To this end, several multi-bit encoded MSs are provided that are of potential interest in single/multi-beam, Orbital Angular Momentum (OAM), and Phase Gradient Metasurface (PGM) antenna applications. In each case, the proposed DNN provides the required unit cell in two transmission and reflection modes from 12 GHz to 18 GHz for different targets. In order to demonstrate the performance of the proposed DNN method, the following steps are implemented: (i) first, the architecture of the DNN method and the creation of pixelated unit cells by DNN are discussed. This step presents 3-bit coded unit cells to design Intelligent Reflective Surfaces (IRS) in wireless communication systems. (ii) several single/multibeam reflective OAM and transmissive PGM high gain antenna structures are presented. (iii) In order to validate the proposed design concept, a Transmissive PGM lens antenna prototype with 22.6 dBi gain and Aperture Efficiency (AE) of 40% is experimentally tested at 14 GHz. Simulation and measurement results are in good agreement, which confirms the accuracy of the proposed method. The proposed DNN method can be considered a new reliable approach for designing reflective/transmissive MSs in applications of antenna and microwave with their highly demanding market.</p>

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Multifunctional Automatic Metasurface Design Using Deep Learning Approach

Mahdi¹,

Ghorbani²,

Beyraghi³

et al. 2022

Preprint

2

0

View full text Add to dashboard Cite

<p>This paper presents a new efficient and robust method for the automatic design of Metasurface (MS) by using a Deep Neural Network (DNN), which has less complexity and design time compared to conventional MS design methods. The main contribution of the present method is its ability the design reflective/transmissive MS for wave manipulation in a wide range of frequencies (12 GHz - 18 GHz). To this end, several multi-bit encoded MSs are provided that are of potential interest in single/multi-beam, Orbital Angular Momentum (OAM), and Phase Gradient Metasurface (PGM) antenna applications. In each case, the proposed DNN provides the required unit cell in two transmission and reflection modes from 12 GHz to 18 GHz for different targets. In order to demonstrate the performance of the proposed DNN method, the following steps are implemented: (i) first, the architecture of the DNN method and the creation of pixelated unit cells by DNN are discussed. This step presents 3-bit coded unit cells to design Intelligent Reflective Surfaces (IRS) in wireless communication systems. (ii) several single/multibeam reflective OAM and transmissive PGM high gain antenna structures are presented. (iii) In order to validate the proposed design concept, a Transmissive PGM lens antenna prototype with 22.6 dBi gain and Aperture Efficiency (AE) of 40% is experimentally tested at 14 GHz. Simulation and measurement results are in good agreement, which confirms the accuracy of the proposed method. The proposed DNN method can be considered a new reliable approach for designing reflective/transmissive MSs in applications of antenna and microwave with their highly demanding market.</p>

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Deep Learning Approach for Target Locating in Through-the-Wall Radar under Electromagnetic Complex Wall

Cited by 3 publications

References 14 publications

Roadmap on signal processing for next generation measurement systems

Roadmap on signal processing for next generation measurement systems

Multifunctional Automatic Metasurface Design Using Deep Learning Approach

Multifunctional Automatic Metasurface Design Using Deep Learning Approach

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