Clutter Mitigation in Range Enhanced Radar Images Using Sparsity Based Denoising Autoencoders

Vishwakarma, Shelly; Pandey, Neeraj Kumar; Ram, Shobha Sundar

doi:10.1109/radar41533.2019.171257

Cited by 2 publications

(2 citation statements)

References 17 publications

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“…지도학습 기반 autoencoder는 레이다 이미지 데이터 denoising에 사용된다. 참고문헌 [10]은 실내 클러터 제거 를 위한 sparsity-based denoising autoencoder를 제안했다 [10] . 해당 논문 저자들은 RF 이미징 센서를 이용하여 동일 장소에 대한 cluttered radar 이미지와 clean radar 이미지를 짝을 지어 학습 데이터셋으로 수집했다.…”

Section: -2 딥러닝 기반 클러터 제거 기법unclassified

Clutter Suppression Technique Using Denoising Encoder-Decoder Deep Learning Network

Choi¹,

Lim²,

Kwon³

et al. 2022

J. Korean Inst. Electromagn. Eng. Sci.

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Clutter is the radar noise signal that is reflected from the elements surrounding the targets. Since clutter degrades the radar system's range and doppler frequency detection capability, clutter suppression is a critical signal-processing algorithm that can improve the performance of a radar system. This paper proposes a ground-clutter suppression method using denoising encoder-decoder deep learning network with dual encoding channels, residual connections, and skip connections. Radar signal dataset pipeline was generated using MATLAB in order to train the network. In this paper, deep learning-based clutter suppression method that can be applied in various operating conditions, is discussed.

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Section: -2 딥러닝 기반 클러터 제거 기법unclassified

Clutter Suppression Technique Using Denoising Encoder-Decoder Deep Learning Network

Choi¹,

Lim²,

Kwon³

et al. 2022

J. Korean Inst. Electromagn. Eng. Sci.

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“…The incorporation of these additional hidden layers increases the computational time complexity during training but there is a significant reduction during test due to the feature size reduction within the hidden layers. Preliminary studies with the StackedSDAE were presented in [47]. Here, we offer a more comprehensive experimental evaluation of the proposed algorithm.…”

Section: Introductionmentioning

confidence: 99%

Sparsity‐based autoencoders for denoising cluttered radar signatures

Ram

Vishwakarma

Sneh

et al. 2021

IET Radar Sonar & Navi

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Narrowband and broadband indoor radar images significantly deteriorate in the presence of target-dependent and target-independent static and dynamic clutter arising from walls. A stacked and sparse denoising autoencoder (StackedSDAE) is proposed for mitigating the wall clutter in indoor radar images. The algorithm relies on the availability of clean images and the corresponding noisy images during training and requires no additional information regarding the wall characteristics. The algorithm is evaluated on simulated Doppler-time spectrograms and high-range resolution profiles generated for diverse radar frequencies and wall characteristics in around-the-corner radar (ACR) scenarios. Additional experiments are performed on range-enhanced frontal images generated from measurements gathered from a wideband radio frequency imaging sensor. The results from the experiments show that the StackedSDAE successfully reconstructs images that closely resemble those that would be obtained in free space conditions. Furthermore, the incorporation of sparsity and depth in the hidden layer representations within the autoencoder makes the algorithm more robust to low signal-to-noise ratio (SNR) and label mismatch between clean and corrupt data during training than the conventional single-layer DAE. For example, the denoised ACR signatures show a structural similarity above 0.75 to clean free space images at SNR of −10 dB and label mismatch error of 50%.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Clutter Mitigation in Range Enhanced Radar Images Using Sparsity Based Denoising Autoencoders

Cited by 2 publications

References 17 publications

Clutter Suppression Technique Using Denoising Encoder-Decoder Deep Learning Network

Clutter Suppression Technique Using Denoising Encoder-Decoder Deep Learning Network

Sparsity‐based autoencoders for denoising cluttered radar signatures

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