A Novel SAR Sidelobe Suppression Method Based on CNN

Yuan, Sen; Yu, Ze; Li, Chunsheng; Wang, Shusen

doi:10.1109/lgrs.2020.2968336

Cited by 16 publications

(7 citation statements)

References 11 publications

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“…To go beyond the simple visual inspection of images of extended targets, the image contrast metric is introduced in this work to evaluate the separation ability of the proposed method and the other DOA approaches. Image contrast shows the differences in the intensity of each pixel of the image or signal, which are used to evaluate the sidelobe suppression on SAR images [44], [45]. Let us suppose that the two extended targets are better separated because of the improved angular resolution.…”

Section: B Complex Extended Targetsmentioning

confidence: 99%

A Novel Approach to Unambiguous Doppler Beam Sharpening for Forward-Looking MIMO Radar

et al. 2022

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The ambiguity problem of targets in Doppler beam sharpening (DBS) with forward-looking radar is considered. While DBS is proposed earlier to improve the angular resolution of the radar while keeping the antenna aperture size limited, such a solution suffers from ambiguities in the case of targets positioned symmetrically with respect to the platform movement. To address this problem, an approach named unambiguous Doppler-based forwardlooking multiple-input multiple-output (MIMO) radar beam sharpening scan (UDFMBSC) is proposed, based on the combination of MIMO processing and DBS. The performance of the proposed method is compared to existing approaches using simulated data with point-like and extended targets. The method is successfully verified using experimental data. Index Terms-Beam scan, Doppler beam sharpening (DBS), forward-looking radar, multiple-input multiple-output (MIMO) radar processing. I. INTRODUCTION T HE success of autonomous vehicles strongly depends on their environment-sensing capabilities. Autonomous vehicles are equipped with multiple sensors, including radars, cameras, and lidars [1]. Among them, radar can provide accurate and direct measurements of the range, relative velocity, and angle of multiple targets; offer a long-range coverage of more than 200 m even in challenging weather or lighting conditions [2]; and be easily packaged behind the optically nontransparent fascia of the vehicle. Therefore, radar is typically used in current ADASs' tasks, such as adaptive cruise control (ACC) [3], forward collision avoidance (FCA) [4], lane-change assistance [5], or evasion assistance [6], and is considered one of the key technologies for next-generation autonomous vehicles [7].

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Section: B Complex Extended Targetsmentioning

confidence: 99%

A Novel Approach to Unambiguous Doppler Beam Sharpening for Forward-Looking MIMO Radar

et al. 2022

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“…As a result, many researchers are studying how to identify ships in HR SAR images (Wang et al, 2016;Li et al, 2017b;Salembier et al, 2018;Du et al, 2019;Lin et al, 2019;Wang et al, 2019;Wang et al, 2020b;Wang et al, 2020c;Wang et al, 2020d;Zhang et al, 2020c;Yasir et al, 2022). However, due to the complicated environment and other difficult issues, such as sidelobes and target defocusing (Chen et al, 2019;Han et al, 2019;Xiong et al, 2019;Yuan et al, 2020), identifying ship targets in HR SAR images is still challenging.…”

Section: Introductionmentioning

confidence: 99%

Multi-scale ship target detection using SAR images based on improved Yolov5

Yasir

Liu

et al. 2023

Front. Mar. Sci.

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Synthetic aperture radar (SAR) imaging is used to identify ships, which is a vital task in the maritime industry for managing maritime fisheries, marine transit, and rescue operations. However, some problems, like complex background interferences, various size ship feature variations, and indistinct tiny ship characteristics, continue to be challenges that tend to defy accuracy improvements in SAR ship detection. This research study for multiscale SAR ships detection has developed an upgraded YOLOv5s technique to address these issues. Using the C3 and FPN + PAN structures and attention mechanism, the generic YOLOv5 model has been enhanced in the backbone and neck section to achieve high identification rates. The SAR ship detection datasets and AirSARship datasets, along with two SAR large scene images acquired from the Chinese GF-3 satellite, are utilized to determine the experimental results. This model’s applicability is assessed using a variety of validation metrics, including accuracy, different training and test sets, and TF values, as well as comparisons with other cutting-edge classification models (ARPN, DAPN, Quad-FPN, HR-SDNet, Grid R-CNN, Cascade R-CNN, Multi-Stage YOLOv4-LITE, EfficientDet, Free-Anchor, Lite-Yolov5). The performance values demonstrate that the suggested model performed superior to the benchmark model used in this study, with higher identification rates. Additionally, these excellent identification rates demonstrate the recommended model’s applicability for maritime surveillance.

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“…Spatially variant apodization (SVA) is introduced by Stankwitz et al. [6] for reducing impulse response sidelobe levels without any mainlobe broadening by using an image location‐dependent weighting function [7, 8]. It is worth noting that SVA‐like algorithms cannot suppress additive noise.…”

Section: Introductionmentioning

confidence: 99%

“…Sidelobes in SAR imagery have traditionally been reduced by applying an amplitude weighting function to the data at the expense of the mainlobe width. Spatially variant apodization (SVA) is introduced by Stankwitz et al [6] for reducing impulse response sidelobe levels without any mainlobe broadening by using an image locationdependent weighting function [7,8]. It is worth noting that SVA-like algorithms cannot suppress additive noise.…”

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confidence: 99%

Sparse regularization method combining SVA for feature enhancement of SAR images

Zhou

Zhang

et al. 2022

Electronics Letters

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Sparse signal processing has been widely used in synthetic aperture radar imaging and feature enhancement of images in the recent decade. Sparse regularization 1 can reduce the imaging noise level and suppress sidelobes. However, the suppression of sidelobes by sparse regularization often pays the price of losing information of weak targets. Therefore, the sparse regularization method combining spatially variant apodization is proposed in this paper, which can suppress noise, sidelobes and retain detail information. The performance of the proposed method is verified using simulated and real data. Data availability statement:The data that support the findings of this study are available from the corresponding author upon reasonable request.

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A Novel SAR Sidelobe Suppression Method Based on CNN

Cited by 16 publications

References 11 publications

A Novel Approach to Unambiguous Doppler Beam Sharpening for Forward-Looking MIMO Radar

A Novel Approach to Unambiguous Doppler Beam Sharpening for Forward-Looking MIMO Radar

Multi-scale ship target detection using SAR images based on improved Yolov5

Sparse regularization method combining SVA for feature enhancement of SAR images

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