Forward-looking super-resolution radar imaging via reweighted L1-minimization

Lee, Hyukjung; Chun, Joohwan; Song, Sung-Chan

doi:10.1109/radar.2018.8378601

Cited by 4 publications

(9 citation statements)

References 9 publications

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“…Because effects of 3‐D acceleration on both observed echo and over‐complete dictionary are not considered in the reference algorithm, the reconstruction result in Figure 7b cannot reflect any information of the forward‐looking scene. Subsequently, another reference algorithm [40] is adopted to handle the complicated forward‐looking geometry, as shown in Figure 7c. Due to the ignorance of 3‐D acceleration, conventional reweighted L1‐minimisation cannot describe the modified platform‐scene geometry.…”

Section: Simulation Resultsmentioning

confidence: 99%

“… Surface target simulation for manoeuvering forward‐looking imaging under different SNR conditions. (a) True sparse synthetic aperture radar (SAR) image; (b) Reference algorithm [35] with SNR = 25 dB; (c) Reference algorithm [40] with SNR = 25 dB; (d) The proposed method with SNR = 25 dB; (e) The proposed method with SNR = 0 dB; (f) The proposed method with SNR = −15 dB …”

Section: Simulation Resultsmentioning

confidence: 99%

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Super‐resolution forward‐looking imaging method for manoeuvering platform with optimised dictionary and extended sparsity adaptive matching pursuit

Zhang

Liang

Chen³

et al. 2022

IET Radar Sonar & Navi

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In forward‐looking imaging, left‐right Doppler is ambiguous and cross‐range resolution is limited by real aperture, which result in traditional synthetic aperture radar imaging methods no longer applicable. Besides, the impact of three‐dimensional acceleration on an over‐complete dictionary matrix and sparse reconstruction kernel is not considered in conventional super‐resolution forward‐looking imaging methods. To solve these problems, a super‐resolution forward‐looking imaging method for manoeuvering platforms is proposed. First, the range walk caused by forward‐looking geometry and higher‐order phase introduced by three‐dimensional acceleration are eliminated by compensating a pre‐processing function. Subsequently, according to beam steering‐based geometry and linear regression model, the over‐complete dictionary matrix is optimised to more accurately describe the complicated forward‐looking geometry with curved trajectory. Finally, sparsity adaptive matching pursuit kernel with an improved halting condition is adopted to reconstruct the forward‐looking scene. The proposed method is supported by point target simulation and surface target simulation.

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Section: Simulation Resultsmentioning

confidence: 99%

Section: Simulation Resultsmentioning

confidence: 99%

Super‐resolution forward‐looking imaging method for manoeuvering platform with optimised dictionary and extended sparsity adaptive matching pursuit

Zhang

Liang

Chen³

et al. 2022

IET Radar Sonar & Navi

View full text Add to dashboard Cite

show abstract

“…In addition, when the FMCW MIMO radars are spatially distributed, each radar should perform the subspace estimation, which is not desirable for radars with limited computing resources. To avoid the subspace estimation, compressive sensing (CS) based approaches have been investigated [10][11][12][13][14][15]. In [10], the reweighted L1 minimisation method is applied to radar imaging with a single antenna FMCW radar.…”

Section: Introductionmentioning

confidence: 99%

“…To avoid the subspace estimation, compressive sensing (CS) based approaches have been investigated [10][11][12][13][14][15]. In [10], the reweighted L1 minimisation method is applied to radar imaging with a single antenna FMCW radar. In [11], greedy reconstruction algorithms such as orthogonal matching pursuit (OMP) or subspace pursuit are exploited to achieve the radar images, and in [12,13], a sparse Bayesian learning algorithm is applied in the angle-of-arrival estimation.…”

Section: Introductionmentioning

confidence: 99%

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Transfer learning‐based radar imaging with deep convolutional neural networks for distributed frequency modulated continuous waveform multiple‐input multiple‐output radars

Seo

Yang

Hong

et al. 2021

IET Radar Sonar & Navi

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Deep-learning-based radar imaging is developed with distributed frequency modulated continuous waveform multiple-input multiple-output (FMCW MIMO) radars in which a deep-learning approach based on the convolutional neural network (CNN) is proposed to achieve radar images robust to adverse circumstances. Differently from the existing deeplearning methods applied to radar object recognition, the deramped radar signal is exploited as the input of the proposed deep CNN (DCNN) without any processing related to the spectrogram transform and the subspace decomposition. To effectively train the proposed DCNN, the received signal is reformulated in terms of the reflection gain values in the (azimuth, range) patches in the image region of interest such that the output vector of the DCNN is composed of the reflection gain values in the associated patches. Furthermore, to overcome the limitations on the amount of training data and training time, the transfer learning approach is effectively applied to the distributed FMCW MIMO radar imaging. The proposed radar imaging is assessed with synthetic simulation data. Specifically, by transferring the pretrained DCNN model for a given reference radar to other distributed radars, the distributed radars can save about 52.4 % in training time compared with a DCNN having the same architecture but without transfer learning.

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