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Adam M. Smith - IEEE Xplore Author Profile

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Author details

Adam M. Smith

Publications

10

Citations

367

Publications by Year

20102022

Co-Authors:

Batu AytemizKenneth ChangTamara DuplantisJeffrey HeerBill Howe

Show All Co-Authors (14)

Adam M. Smith

Affiliation

Computational Media Department

University of California Santa Cruz

Santa Cruz, CA, USA

Publication Topics

Game Design,
Game Development,
Human Players,
Gameplay,
Video Games,
Constraint Satisfaction Problem,
Exploration Algorithm,
Human Data,
Machine Learning,
Monte Carlo Tree Search,
Rapidly-exploring Random Tree,
Version Of The Game

Biography

Adam M. Smith (S'10) received the B.S. degree in computer science from the University of California Santa Cruz, Santa Cruz, in 2005, where he is currently working towards the Ph.D. degree in computer science.,He has had internships at Terracom Communications (Rwanda), NASA Ames Research Center, Los Alamos National Lab, and Google. His research is centered on computational creativity in game design but also includes contributions in computer vision and statistical machine learning.,Mr. Smith is a student member of the Association for Computing Machinery (ACM), the Association for the Advancement of Artificial Intelligence (AAAI), and the International Game Developers Association (IGDA), and an artist in the TOPLAP algorithmic music livecodin...

Publications

10

Citations

367

Publications by Year

20102022

Co-Authors:

Batu Aytemiz
Kenneth Chang
Tamara Duplantis
Jeffrey Heer
Bill Howe

Show All Co-Authors (14)

Author's Published Works

Search History

Showing 1-25 of 173 results

Conferences (112)

Journals (57)

Early Access Articles (3)

Magazines (1)

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Jun Shi(172)
Xiaoling Zhang(137)
Shunjun Wei(135)
Mou Wang(37)
Chen Wang(23)
School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, China(76)
School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, P.R.China(11)
University of Electronic Science and Technology of China, Chengdu, China(11)
School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, P.R. China(8)
School of Electronic Engineering, University of Electronic Science and Technology of China, Chengdu, China(7)
IEEE Transactions on Geoscience and Remote Sensing(24)
IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium(14)
IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium(11)
2019 6th Asia-Pacific Conference on Synthetic Aperture Radar (APSAR)(10)
IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium(8)
IEEE(173)
Kuala Lumpur, Malaysia(14)
Pasadena, CA, USA(11)
Xiamen, China(10)
Yokohama, Japan(8)
Brussels, Belgium(7)
Synthetic Aperture Radar(131)
Synthetic Aperture Radar Images(86)
Convolutional Neural Network(52)
Imaging Results(48)
3D Images(47)

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Results

Enhancing mmWave Automotive Forward-Looking MIMO-SAR Imaging by Space-Time Sparsity

Yanqin Xu;Xiaoling Zhang;Xiaowo Xu;Shunjun Wei;Jun Shi;Tianjiao Zeng;Kah Chan Teh;Tee Hiang Cheng

IEEE Transactions on Aerospace and Electronic Systems

Year: 2025 | Early Access Article |

Millimeter-wave (mmWave) automotive multiple-input multiple-output forward looking synthetic aperture radar technology (FL-MIMO-SAR) can achieve high-resolution images for advanced driver assistance systems (ADAS). However, the conventional FL-MIMO-SAR imaging methods suffer from high sidelobe, noise interference, and the relatively low imaging resolution in the FL region. To tackle these challeng...Show More

Enhancing mmWave Automotive Forward-Looking MIMO-SAR Imaging by Space-Time Sparsity

Yanqin Xu;Xiaoling Zhang;Xiaowo Xu;Shunjun Wei;Jun Shi;Tianjiao Zeng;Kah Chan Teh;Tee Hiang Cheng

IEEE Transactions on Aerospace and Electronic Systems

Year: 2025 | Early Access Article |

A Fast Lq Sparsity-Driven Method With Adaptive-Focusing Framework for mmWave Automotive Radar Super-Resolution Imaging

Yanqin Xu;Xiaoling Zhang;Shunjun Wei;Jun Shi;Tianjiao Zeng;Xu Zhan;Tianwen Zhang;Xiaowo Xu

IEEE Transactions on Intelligent Transportation Systems

Year: 2025 | Early Access Article |

Millimeter-wave (mmW) automotive radar imaging technology shows significant promise in advanced driver assistance systems (ADAS). Super-resolution imaging methods can be employed the limited aperture length of automotive radar to improve azimuth (angular) resolution. However, automotive radar images typically exhibit large dynamic range (LDR) and large scene (LS), leading to pay extensive computat...Show More

A Fast Lq Sparsity-Driven Method With Adaptive-Focusing Framework for mmWave Automotive Radar Super-Resolution Imaging

Yanqin Xu;Xiaoling Zhang;Shunjun Wei;Jun Shi;Tianjiao Zeng;Xu Zhan;Tianwen Zhang;Xiaowo Xu

IEEE Transactions on Intelligent Transportation Systems

Year: 2025 | Early Access Article |

Unsupervised 3D SAR Imaging Network Based on Generative Adversary Learning

Mou Wang;Yifei Hu;Shunjun Wei;Jiahui Li;Rong Shen;Jun Shi;Guolong Cui;Lingjiang Kong;Yongxin Guo

IEEE Transactions on Antennas and Propagation

Year: 2025 | Early Access Article |

Reconstructing three-dimensional synthetic aperture radar (3D SAR) images from the sparsely sampled echo measurements holds significant importance in simplifying the system complexity and sensing costs. The conventional compressed sensing-based imaging algorithms address the problem by suspecting the sparsity of the imaging space, showing good reconstruction performance but failing to tackle the i...Show More

Unsupervised 3D SAR Imaging Network Based on Generative Adversary Learning

Mou Wang;Yifei Hu;Shunjun Wei;Jiahui Li;Rong Shen;Jun Shi;Guolong Cui;Lingjiang Kong;Yongxin Guo

IEEE Transactions on Antennas and Propagation

Year: 2025 | Early Access Article |

SA-ISAR Imaging via Detail Enhancement Operator and Adaptive Threshold Sensing

Mou Wang;Yanbo Wen;Shunjun Wei;Jiangbo Hu;Wei Yi;Jun Shi

IEEE Transactions on Geoscience and Remote Sensing

Year: 2025 | Volume: 63 | Journal Article |

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Sparse aperture inverse synthetic aperture radar (SA-ISAR) aims to reconstruct target images by undersampled data. Traditional algorithms are limited in their application scope and exhibit weak capabilities in reconstructing target details. To address these issues, an adaptive threshold sensing (ATS) sparse reconstruction algorithm based on alternating direction method of multiplier (ADMM), named ...Show More

SA-ISAR Imaging via Detail Enhancement Operator and Adaptive Threshold Sensing

Mou Wang;Yanbo Wen;Shunjun Wei;Jiangbo Hu;Wei Yi;Jun Shi

IEEE Transactions on Geoscience and Remote Sensing

Year: 2025 | Volume: 63 | Journal Article |

Exploring Spatial Feature Regularization in Deep-Learning-Based TomoSAR Reconstruction: A Preliminary Study and Performance Analysis

Tianjiao Zeng;Xu Zhan;Yu Ren;Xiangdong Ma;Liang Liu;Jun Shi;Shunjun Wei;Mou Wang;Xiaoling Zhang

IEEE Transactions on Geoscience and Remote Sensing

Year: 2025 | Volume: 63 | Journal Article |

Cited by: Papers (1)

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Tomographic synthetic aperture radar (TomoSAR) shows great potential for high-quality 3-D mapping, especially in urban areas. As TomoSAR reconstruction methods advance into the deep learning (DL) era, current studies have demonstrated DL’s strengths in both precision and efficiency. However, for reconstructing urban areas with prominent spatial features from building structures, current studies fo...Show More

Exploring Spatial Feature Regularization in Deep-Learning-Based TomoSAR Reconstruction: A Preliminary Study and Performance Analysis

Tianjiao Zeng;Xu Zhan;Yu Ren;Xiangdong Ma;Liang Liu;Jun Shi;Shunjun Wei;Mou Wang;Xiaoling Zhang

IEEE Transactions on Geoscience and Remote Sensing

Year: 2025 | Volume: 63 | Journal Article |

Synthetic Aperture Radar Imaging Meets Deep Unfolded Learning: A comprehensive review

Mou Wang;Yifei Hu;Shunjun Wei;Jun Shi;Guolong Cui;Lingjiang Kong;Yongxin Guo

IEEE Geoscience and Remote Sensing Magazine

Year: 2025 | Volume: 13, Issue: 1 | Magazine Article |

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Synthetic aperture radar (SAR) can obtain high-resolution images without being affected by environmental visibility. The compressed sensing (CS) technique is considered to be a strong candidate for simplifying SAR system complexity and improving imaging quality. With CS, SAR imaging is addressed by optimizing an augmented object function with data fidelity and feature-oriented priors; however, it ...Show More

Synthetic Aperture Radar Imaging Meets Deep Unfolded Learning: A comprehensive review

Mou Wang;Yifei Hu;Shunjun Wei;Jun Shi;Guolong Cui;Lingjiang Kong;Yongxin Guo

IEEE Geoscience and Remote Sensing Magazine

Year: 2025 | Volume: 13, Issue: 1 | Magazine Article |

Spatiotemporal Staggered Projection Imaging for Video-SAR Under the Resource-Constrained Platform

Xiaqing Yang;Jun Shi;Bingjie Zhang;Chuanfei Zang;Pin Li;Le Xin;Yihang Zhou;Junjie Wu

IEEE Transactions on Aerospace and Electronic Systems

Year: 2025 | Volume: 61, Issue: 2 | Journal Article |

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This article focuses on a reliable and high-resolution synthetic aperture radar (SAR) imaging algorithm for lightweight autonomous aerial vehicle (AAV)-borne video-SAR. By combining a spatiotemporal staggered sampling technique and a multiframe enhancement reconstruction network, the proposed spatiotemporal staggered projection algorithm can solve the contradiction between low data transmission ra...Show More

Spatiotemporal Staggered Projection Imaging for Video-SAR Under the Resource-Constrained Platform

Xiaqing Yang;Jun Shi;Bingjie Zhang;Chuanfei Zang;Pin Li;Le Xin;Yihang Zhou;Junjie Wu

IEEE Transactions on Aerospace and Electronic Systems

Year: 2025 | Volume: 61, Issue: 2 | Journal Article |

CMTI: Non-Line-of-Sight Radar Imaging for Non-Cooperative Corner Motion Target

Yanbo Wen;Shunjun Wei;Xiang Cai;Rong Shen;Mou Wang;Jun Shi;Guolong Cui

IEEE Transactions on Vehicular Technology

Year: 2025 | Volume: 74, Issue: 1 | Journal Article |

Cited by: Papers (6)

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Concealed moving target imaging is an important task in the fields of autonomous driving, urban combat, city anti-terrorism, etc. However, existing radar imaging techniques mainly focus on line-of-sight (LOS) targets and struggle to sense non-line-of-sight (NLOS) moving targets. In this paper, a joint NLOS model and inverse synthetic aperture radar (ISAR) algorithm, dubbed corner motion target ima...Show More

CMTI: Non-Line-of-Sight Radar Imaging for Non-Cooperative Corner Motion Target

Yanbo Wen;Shunjun Wei;Xiang Cai;Rong Shen;Mou Wang;Jun Shi;Guolong Cui

IEEE Transactions on Vehicular Technology

Year: 2025 | Volume: 74, Issue: 1 | Journal Article |

Joint Generalized Lq and Convolutional Regularization: Enhancing mmW Automotive SAR Sparse Imaging

Yanqin Xu;Xiaoling Zhang;Shunjun Wei;Jun Shi;Tianjiao Zeng;Xiaowo Xu;Wensi Zhang;Xu Zhan

IEEE Transactions on Geoscience and Remote Sensing

Year: 2024 | Volume: 62 | Journal Article |

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Millimeter-wave (mmW) automotive synthetic aperture radar (Auto-SAR) technology holds significant promise for advanced driver assistance systems (ADASs). Sparse imaging methods can improve the quality of Auto-SAR images, such as suppressing sidelobes and noise. However, the

$l_{1}$ convex regularization-based sparse imaging methods suffer from the bias estimation, which reduces the target amplit...Show More

Joint Generalized Lq and Convolutional Regularization: Enhancing mmW Automotive SAR Sparse Imaging

Yanqin Xu;Xiaoling Zhang;Shunjun Wei;Jun Shi;Tianjiao Zeng;Xiaowo Xu;Wensi Zhang;Xu Zhan

IEEE Transactions on Geoscience and Remote Sensing

Year: 2024 | Volume: 62 | Journal Article |

Distribution-Based Anchor Assignment and Comprehensive Score Voting With Distance Penalty IoU Loss for SAR Remote Sensing Ship Detection

Xiaowo Xu;Xiaoling Zhang;Tianwen Zhang;Wensi Zhang;Jun Shi;Shunjun Wei;Zikang Shao;Yanqin Xu;Tianjiao Zeng

IEEE Transactions on Instrumentation and Measurement

Year: 2024 | Volume: 73 | Journal Article |

Cited by: Papers (1)

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Ship detection in synthetic aperture radar (SAR) remote sensing images is a fundamental yet challenging task in Earth observation and measurement. However, existing deep learning (DL)-based SAR ship detectors mostly struggle with unreasonable anchor assignment and limited localization quality, hindering the further accuracy increase of ship detection in SAR remote sensing images. To address the ab...Show More

Distribution-Based Anchor Assignment and Comprehensive Score Voting With Distance Penalty IoU Loss for SAR Remote Sensing Ship Detection

Xiaowo Xu;Xiaoling Zhang;Tianwen Zhang;Wensi Zhang;Jun Shi;Shunjun Wei;Zikang Shao;Yanqin Xu;Tianjiao Zeng

IEEE Transactions on Instrumentation and Measurement

Year: 2024 | Volume: 73 | Journal Article |

Unsupervised 3-D Array-SAR Imaging Based on Generative Model for Scattering Diagnosis

Tianjiao Zeng;Xu Zhan;Xiangdong Ma;Rui Liu;Jun Shi;Shunjun Wei;Mou Wang;Xiaoling Zhang

IEEE Antennas and Wireless Propagation Letters

Year: 2024 | Volume: 23, Issue: 8 | Journal Article |

Cited by: Papers (3)

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Scattering diagnosis requires the spatial distribution of the target's scattering coefficient, which can be obtained through radar imaging, specifically 3-D array synthetic aperture radar, known for its high-quality, flexible measurements. Recent advancements in sparsity-based imaging methods have addressed traditional method limitations like limited resolution and interferences. However, they pre...Show More

Unsupervised 3-D Array-SAR Imaging Based on Generative Model for Scattering Diagnosis

Tianjiao Zeng;Xu Zhan;Xiangdong Ma;Rui Liu;Jun Shi;Shunjun Wei;Mou Wang;Xiaoling Zhang

IEEE Antennas and Wireless Propagation Letters

Year: 2024 | Volume: 23, Issue: 8 | Journal Article |

GNN-JFL: Graph Neural Network for Video SAR Shadow Tracking With Joint Motion-Appearance Feature Learning

Wensi Zhang;Xiaoling Zhang;Xiaowo Xu;Yanqing Xu;Zikang Shao;Jun Shi;Shunjun Wei;Tianjiao Zeng

IEEE Transactions on Geoscience and Remote Sensing

Year: 2024 | Volume: 62 | Journal Article |

Cited by: Papers (5)

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In this study, we address the challenges associated with video synthetic aperture radar (Video SAR) shadow tracking, a technique used for continuous monitoring of ground moving targets. Due to challenges such as changes in shadow appearance, low contrast between shadow and background, and scene occlusion in Video SAR, existing methods often encounter extensive matching errors in the data associati...Show More

GNN-JFL: Graph Neural Network for Video SAR Shadow Tracking With Joint Motion-Appearance Feature Learning

Wensi Zhang;Xiaoling Zhang;Xiaowo Xu;Yanqing Xu;Zikang Shao;Jun Shi;Shunjun Wei;Tianjiao Zeng

IEEE Transactions on Geoscience and Remote Sensing

Year: 2024 | Volume: 62 | Journal Article |

A Novel Back-Projection-Based Target Motion Parameter Estimation Scheme for Dual-Channel SAR

Xinxin Tang;Darong Huang;Chen Wang;Liang Li;Xiaoling Zhang;Jun Shi;Shunjun Wei

IEEE Transactions on Geoscience and Remote Sensing

Year: 2024 | Volume: 62 | Journal Article |

Cited by: Papers (1)

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Due to the reduction of imaging accuracy caused by the approximations of signal models, traditional synthetic aperture radar (SAR) moving target motion parameter estimation methods based on frequency-domain imaging algorithms may suffer from the problem of accuracy reduction. To solve this problem, a novel moving target motion parameter estimation scheme is proposed for dual-channel SAR based on t...Show More

A Novel Back-Projection-Based Target Motion Parameter Estimation Scheme for Dual-Channel SAR

Xinxin Tang;Darong Huang;Chen Wang;Liang Li;Xiaoling Zhang;Jun Shi;Shunjun Wei

IEEE Transactions on Geoscience and Remote Sensing

Year: 2024 | Volume: 62 | Journal Article |

CFAR-DP-FW: A CFAR-Guided Dual-Polarization Fusion Framework for Large-Scene SAR Ship Detection

Tianjiao Zeng;Tianwen Zhang;Zikang Shao;Xiaowo Xu;Wensi Zhang;Jun Shi;Shunjun Wei;Xiaoling Zhang

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

Year: 2024 | Volume: 17 | Journal Article |

Cited by: Papers (12)

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Effective ship detection in synthetic aperture radar (SAR) imagery is crucial for maritime safety and surveillance. Despite the advancements in deep learning for SAR ship detection, significant challenges remain, particularly in large scenes. These challenges are twofold: the detection of extremely small ships is often hindered by inadequate feature extraction, and the presence of inshore ships is...Show More

CFAR-DP-FW: A CFAR-Guided Dual-Polarization Fusion Framework for Large-Scene SAR Ship Detection

Tianjiao Zeng;Tianwen Zhang;Zikang Shao;Xiaowo Xu;Wensi Zhang;Jun Shi;Shunjun Wei;Xiaoling Zhang

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

Year: 2024 | Volume: 17 | Journal Article |

Dual-Domain Feature-Oriented Interference Suppression for FMCW Automotive Radar

Hao Zhang;Shunjun Wei;Xiang Cai;Lin Nie;Mou Wang;Jun Shi;Guolong Cui

IEEE Sensors Journal

Year: 2024 | Volume: 24, Issue: 5 | Journal Article |

Cited by: Papers (1)

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Frequency-modulated continuous wave (FMCW) radar is an excellent sensor; however, the widespread popularity and application of millimeter-wave (mmW) automotive radar, greatly increases the risk of mutual interference between vehicles, weakening the abilities of automotive radar to sense the environment. As a result, efficiently suppressing interference has been a challenge. To address this issue, ...Show More

Dual-Domain Feature-Oriented Interference Suppression for FMCW Automotive Radar

Hao Zhang;Shunjun Wei;Xiang Cai;Lin Nie;Mou Wang;Jun Shi;Guolong Cui

IEEE Sensors Journal

Year: 2024 | Volume: 24, Issue: 5 | Journal Article |

FUAS-Net: Feature-Oriented Unsupervised Network for FMCW Radar Interference Suppression

Hao Zhang;Shunjun Wei;Mou Wang;Yifei Hu;Jun Shi;Guolong Cui

IEEE Transactions on Microwave Theory and Techniques

Year: 2024 | Volume: 72, Issue: 4 | Journal Article |

Cited by: Papers (3)

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The widespread application of frequency-modulated continuous-wave (FMCW) radars leads to a significant increase in the risk of mutual interference. Conventional compressed sensing (CS)-based interference mitigation methods are limited by large computational capacity, troublesome parameter settings, and inefficient data processing. The supervised deep learning approach can overcome the above challe...Show More

FUAS-Net: Feature-Oriented Unsupervised Network for FMCW Radar Interference Suppression

Hao Zhang;Shunjun Wei;Mou Wang;Yifei Hu;Jun Shi;Guolong Cui

IEEE Transactions on Microwave Theory and Techniques

Year: 2024 | Volume: 72, Issue: 4 | Journal Article |

CTV-Net: Complex-Valued TV-Driven Network With Nested Topology for 3-D SAR Imaging

Mou Wang;Shunjun Wei;Zichen Zhou;Jun Shi;Xiaoling Zhang;Yongxin Guo

IEEE Transactions on Neural Networks and Learning Systems

Year: 2024 | Volume: 35, Issue: 4 | Journal Article |

Cited by: Papers (10)

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The regularization-based approaches offer promise in improving synthetic aperture radar (SAR) imaging quality while reducing system complexity. However, the widely applied

$\ell _{1}$ regularization model is hindered by their hypothesis of inherent sparsity, causing unreal estimations of surface-like targets. Inspired by the edge-preserving property of total variation (TV), we propose a new comp...Show More

CTV-Net: Complex-Valued TV-Driven Network With Nested Topology for 3-D SAR Imaging

Mou Wang;Shunjun Wei;Zichen Zhou;Jun Shi;Xiaoling Zhang;Yongxin Guo

IEEE Transactions on Neural Networks and Learning Systems

Year: 2024 | Volume: 35, Issue: 4 | Journal Article |

HPII-NET: A High-Precision Interference Identification Network for Spaceborne SAR Images

Lin Nie;Shunjun Wei;Hao Zhang;Yifei Hu;Jun Shi;Xiaoling Zhang

IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium

Year: 2023 | Conference Paper |

Cited by: Papers (1)

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Spaceborne Synthetic Aperture Radar(SAR) can be mounted on space vehicles to collect information on the entire planet with all-day and all-weather imaging capacity. However, the spaceborne SAR sensor may suffer from severe interferences resulting in image degradation, which puts forward an urgent need for interference identification and mitigation. This paper proposes a high-precision interference...Show More

HPII-NET: A High-Precision Interference Identification Network for Spaceborne SAR Images

Lin Nie;Shunjun Wei;Hao Zhang;Yifei Hu;Jun Shi;Xiaoling Zhang

IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium

Year: 2023 | Conference Paper |

Interferometric Phase Restoration for Non-Common Band Imaging Via a Physics-Aware Spectrum Fusion Network

Xiangdong Ma;Xiaoling Zhang;Xu Zhan;Yu Ren;Jun Shi;Shunjun Wei;Tianjiao Zeng

IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium

Year: 2023 | Conference Paper |

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The Synthetic Aperture Radar (SAR) system has undergone significant advancements, transitioning into a multi-functional platform with various modes. This study introduces a novel imaging mode that enables the simultaneous acquisition of height and intensity features, addressing the diverse requirements of different regions. Referred to as non-common band imaging, this mode optimizes bandwidth allo...Show More

Interferometric Phase Restoration for Non-Common Band Imaging Via a Physics-Aware Spectrum Fusion Network

Xiangdong Ma;Xiaoling Zhang;Xu Zhan;Yu Ren;Jun Shi;Shunjun Wei;Tianjiao Zeng

IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium

Year: 2023 | Conference Paper |

An Iterative Multidimensional Feature Reconstruction Network for Tomographic SAR Imaging

Yu Ren;Xiaoling Zhang;Xu Zhan;Jun Shi;Shunjun Wei;Tianjiao Zeng

IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium

Year: 2023 | Conference Paper |

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The tomographic Synthetic Aperture Radar (SAR) reconstruction based on deep learning (DL) can achieve high precision and efficiency, making it a promising method for various applications. However, current deep learning-based methods perform reconstruction by separately processing each range-azimuth resolution unit, which limits the utilization of features between resolution units and ignores the t...Show More

An Iterative Multidimensional Feature Reconstruction Network for Tomographic SAR Imaging

Yu Ren;Xiaoling Zhang;Xu Zhan;Jun Shi;Shunjun Wei;Tianjiao Zeng

IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium

Year: 2023 | Conference Paper |

Recent Progress in Sparsity-Regularization Based Imaging Method for Near-Field 3D SAR

Xu Zhan;Xiaoling Zhang;Xiangdong Ma;Shunjun Wei;Jun Shi;Tianjiao Zeng

IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium

Year: 2023 | Conference Paper |

Cited by: Papers (2)

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Near-field three-dimensional synthetic aperture radar (Near-field 3D-SAR) is a powerful imaging technique with diverse applications in scattering diagnosis, person/parcel imaging, building monitoring, forest monitoring, and more. This paper provides an overview of the imaging methods employed in Near-field 3D-SAR, focusing on the underlying methodologies, imaging models, and solving flowcharts. By...Show More

Recent Progress in Sparsity-Regularization Based Imaging Method for Near-Field 3D SAR

Xu Zhan;Xiaoling Zhang;Xiangdong Ma;Shunjun Wei;Jun Shi;Tianjiao Zeng

IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium

Year: 2023 | Conference Paper |

Joint Target Recognition for Multi-Station ISAR via MIIR Network

Yanbo Wen;Shunjun Wei;Hao Zhang;Jun Shi;Xiaoling Zhang

IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium

Year: 2023 | Conference Paper |

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Inverse Synthetic Aperture Radar (ISAR) target recognition is an important branch of ISAR image research. The traditional ISAR recognition mission is done based on monostatic radar. However, the monostatic ISAR can only generate a single view image of the target. In this paper, to improve the recognition accuracy, a method of joint target recognition for Multi-station ISAR (MS-ISAR) via Multi-stat...Show More

Joint Target Recognition for Multi-Station ISAR via MIIR Network

Yanbo Wen;Shunjun Wei;Hao Zhang;Jun Shi;Xiaoling Zhang

IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium

Year: 2023 | Conference Paper |

Non-Line-of-Sight ISAR Imaging Via Millimeter-Wave Automotive Radar

Yanbo Wen;Shunjun Wei;Xinyuan Liu;Xiang Cai;Jun Shi;Xiaoling Zhang

IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium

Year: 2023 | Conference Paper |

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Non-line-of-sight (NLOS) imaging of moving targets is of tremendous interest in the fields of urban sensing and autonomous driving. In this paper, a novel NLOS inverse synthetic aperture radar (ISAR) imaging method is proposed for moving targets by automotive millimeter-wave (MMW). In this scheme, an imaging model of the moving target in urban scenes is developed and analyzed. A low-frequency filt...Show More

Non-Line-of-Sight ISAR Imaging Via Millimeter-Wave Automotive Radar

Yanbo Wen;Shunjun Wei;Xinyuan Liu;Xiang Cai;Jun Shi;Xiaoling Zhang

IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium

Year: 2023 | Conference Paper |

Near-Field 3d Sar Interpretation Based on Lidar and Sar Calibration

Baoyou Wang;Xiaoling Zhang;Jun Shi;Shunjun Wei;Tianjiao Zeng

IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium

Year: 2023 | Conference Paper |

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The near-field array three-dimensional SAR can obtain the three-dimensional electromagnetic scattering characteristics of targets and present the imaging results in the form of point cloud. In recent years, it has been gradually used in the field of target RCS measurement. However, there are some problems in near-field array three-dimensional SAR images, such as interference, sidelobe and missing ...Show More

Near-Field 3d Sar Interpretation Based on Lidar and Sar Calibration

Baoyou Wang;Xiaoling Zhang;Jun Shi;Shunjun Wei;Tianjiao Zeng

IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium

Year: 2023 | Conference Paper |

O-Unet: An Octree-Based Convolutional Neural Network for 3-D Radar Point Clouds Reconstruction

Yi-Fei Hu;Shun-Jun Wei;Jun Shi;Xiao-ling Zhang

IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium

Year: 2023 | Conference Paper |

Cited by: Papers (2)

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Point cloud upsampling and surface reconstruction work has important implications in model generation and target recognition, indoor navigation and autonomous driving. In this paper, an auto-encoder(AE) network based on octree convolution network are proposed for target surface reconstruction with millimeter-wave (MMW) radar and LIDAR. In the scheme, we learn to generate point cloud data by a two-...Show More

O-Unet: An Octree-Based Convolutional Neural Network for 3-D Radar Point Clouds Reconstruction

Yi-Fei Hu;Shun-Jun Wei;Jun Shi;Xiao-ling Zhang

IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium

Year: 2023 | Conference Paper |

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