Hiroaki Yamada - IEEE Xplore Author Profile
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Hiroaki Yamada

Publications
2
Citations
5
Publications by Year
20172020
Co-Authors:
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Hiroaki Yamada

Affiliation

Fujitsu Laboratories Ltd., Kawasaki, JAPAN

Publication Topics

Biography

Hiroaki Yamada is a Researcher at the Artificial Intelligence Laboratory at Fujitsu Laboratories Ltd., Japan. He holds MS and PhD degrees in Knowledge Science from Japan Advanced Institute of Science and Technology. His research interests include complex systems science, agent-based modeling and simulation, and computational social science. His email address is yamadah@fujitsu.com.(Based on document published on 29 March 2021).
Publications
2
Citations
5
Publications by Year
20172020
Co-Authors:
Show All Co-Authors (6)
Author's Published Works

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A Rapid Radar Signal Sorting Method Based on Density and Gaussian Mixture Model Data Stream Clustering

Weibo Huo;Haoyang Yu;Yujie Zhang;Gengchen Xu;Jifang Pei;Yin Zhang;Yulin Huang

IEEE Transactions on Aerospace and Electronic Systems
Year: 2025 | Early Access Article |
Radar signal sorting is one of the crucial techniques in radar reconnaissance. However, as the electromagnetic environment increasingly complex and the density of radar pulses surges, the efficiency of clustering-based sorting algorithms is severely degraded. To better align with the streaming data characteristics of radar pulses and avoid the storage and computation of large amounts of pulse data...Show More

A Rapid Radar Signal Sorting Method Based on Density and Gaussian Mixture Model Data Stream Clustering

Weibo Huo;Haoyang Yu;Yujie Zhang;Gengchen Xu;Jifang Pei;Yin Zhang;Yulin Huang

Year: 2025 | Early Access Article |

KiRV: Robust Human Identification Via Multi-Modal Learning Based on Kinetic Gait Features of Radar and Vision

Lang Deng;Jifang Pei;Yuansen Song;Weibo Huo;Yin Zhang;Yulin Huang

IEEE Internet of Things Journal
Year: 2025 | Early Access Article |
Gait is an appealing biometric pattern that aims to identify individuals based on the way they walk. Gait recognition, a passive human identification technology utilized from a distance without subject cooperation, plays a considerable role in life monitoring, crime prevention, security guarantee, and other identity recognition applications. Although vision-based methods dominate the state-of-the-...Show More

KiRV: Robust Human Identification Via Multi-Modal Learning Based on Kinetic Gait Features of Radar and Vision

Lang Deng;Jifang Pei;Yuansen Song;Weibo Huo;Yin Zhang;Yulin Huang

Year: 2025 | Early Access Article |

FlexiRL: An Integrated Jamming Strategy Framework for Hybrid Mode and Parameter Decision-Making

Yujie Zhang;Cui Zhang;Weibo Huo;Yulin Huang;Rundong Jiang;Jifang Pei;Yin Zhang

IEEE Transactions on Aerospace and Electronic Systems
Year: 2025 | Early Access Article |
Electronic warfare holds increasing importance in modern military operations. Effective jamming decision-making is capable of supplying timely electronic protection for high-value targets. Developing a powerful jamming strategy requires selecting appropriate jamming modes and optimizing jamming parameters. To achieve simultaneous optimization of jamming modes and multidomain parameters, a jamming ...Show More

FlexiRL: An Integrated Jamming Strategy Framework for Hybrid Mode and Parameter Decision-Making

Yujie Zhang;Cui Zhang;Weibo Huo;Yulin Huang;Rundong Jiang;Jifang Pei;Yin Zhang

Year: 2025 | Early Access Article |

SAR-PAA: A Physically Adversarial Attack Approach Against SAR Intelligent Target Recognition

Yanjing Ma;Jifang Pei;Weibo Huo;Yin Zhang;Yulin Huang;Keyang Chen;Jianyu Yang

IEEE Transactions on Aerospace and Electronic Systems
Year: 2025 | Volume: 61, Issue: 2 | Journal Article |
Cited by: Papers (1)
Synthetic aperture radar (SAR) target recognition has entered a new era of intelligence due to the rapid development of deep learning. Naturally, an accompanying challenge arises in countering SAR intelligent target recognition technology and protecting the targets of interest from exposure risks. In this article, a novel adversarial attack approach against SAR intelligent target recognition is pr...Show More

SAR-PAA: A Physically Adversarial Attack Approach Against SAR Intelligent Target Recognition

Yanjing Ma;Jifang Pei;Weibo Huo;Yin Zhang;Yulin Huang;Keyang Chen;Jianyu Yang

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

Airborne Radar Operating State Recognition Based on Multi-Parameter Fusion RCS-Conv Method

Xu Zhou;Hao Mu;Yujie Zhang;Xinjie Shi;Weibo Huo;Jianyu Yang;Jianqi Wu

2024 IEEE International Conference on Signal, Information and Data Processing (ICSIDP)
Year: 2024 | Conference Paper |
Airborne radar operating state directly reflects the combat intention of the target platform, playing a crucial role in electronic warfare. However, the challenge of parameter overlap among different operating states poses a new obstacle to radar state recognition. To enhance the capability of recognizing radar operating states with overlapping parameters, a multi-parameter fusion residual coordin...Show More

Airborne Radar Operating State Recognition Based on Multi-Parameter Fusion RCS-Conv Method

Xu Zhou;Hao Mu;Yujie Zhang;Xinjie Shi;Weibo Huo;Jianyu Yang;Jianqi Wu

Year: 2024 | Conference Paper |

Deceptive Jamming Mitigation Based on Proximal Policy Optimization

Cui Zhang;Ruiyang Li;Hao Mu;Yujie Zhang;Weibo Huo;Yin Zhang

2024 IEEE International Conference on Signal, Information and Data Processing (ICSIDP)
Year: 2024 | Conference Paper |
With the increasing reliance on radar technology in modern warfare, the operational capability of radar in countermeasure environments has become increasingly important. For waveform agile radars, randomly choosing transmit waveforms from a large set of orthogonal waveforms is not the optimal approach to mitigate the interference signal. Aiming at deception jamming mitigation, an anti-interference...Show More

Deceptive Jamming Mitigation Based on Proximal Policy Optimization

Cui Zhang;Ruiyang Li;Hao Mu;Yujie Zhang;Weibo Huo;Yin Zhang

Year: 2024 | Conference Paper |

SAR Image Reconstruction Method for Target Detection Using Self-Attention CNN-Based Deep Prior Learning

Min Li;Weibo Huo;Junjie Wu;Jianyu Yang

IEEE Transactions on Geoscience and Remote Sensing
Year: 2024 | Volume: 62 | Journal Article |
Due to its all-day and all-weather capability, synthetic aperture radar (SAR) plays an important role in many remote sensing and monitoring applications. However, conventional SAR image reconstruction methods generally perform undifferentiated imaging, complicating target detection. To address this challenge, we propose a SAR image reconstruction method based on self-attention deep prior learning ...Show More

SAR Image Reconstruction Method for Target Detection Using Self-Attention CNN-Based Deep Prior Learning

Min Li;Weibo Huo;Junjie Wu;Jianyu Yang

Year: 2024 | Volume: 62 | Journal Article |

A Novel SAR Target Recognition Approach under Imbalanced Categories: Constraint and Optimization

Yanjing Ma;Xing Zhang;Jifang Pei;Weibo Huo;Yin Zhang;Yuling Huang;Jianyu Yang

IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium
Year: 2024 | Conference Paper |
Target recognition is one of the most significant tasks in synthetic aperture radar (SAR) image interpretation. However, due to the varying difficulty in acquiring SAR images for different categories, SAR target recognition often encounters the issue of categories imbalance. This make majority categories contribute more to the loss than minority categories, yielding a decline in classification per...Show More

A Novel SAR Target Recognition Approach under Imbalanced Categories: Constraint and Optimization

Yanjing Ma;Xing Zhang;Jifang Pei;Weibo Huo;Yin Zhang;Yuling Huang;Jianyu Yang

Year: 2024 | Conference Paper |

Cascaded Feature Fusion Pyramid Network for Ship Detection in Dualpolarization SAR Images

Xue Tang;Yuanzhe Shang;Honglin Xu;Jifang Pei;Yin Zhang;Weibo Huo;Yulin Huang

IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium
Year: 2024 | Conference Paper |
Cited by: Papers (1)
Synthetic aperture radar (SAR) has been widely applied in maritime target detection. However, most existing SAR ship detection algorithms based on convolutional neural network (CNN) only use single polarization SAR images for detection, neglecting to further improve the detection performance by utilizing the rich polarization information of the SAR images. To deal with this issue, this paper propo...Show More

Cascaded Feature Fusion Pyramid Network for Ship Detection in Dualpolarization SAR Images

Xue Tang;Yuanzhe Shang;Honglin Xu;Jifang Pei;Yin Zhang;Weibo Huo;Yulin Huang

Year: 2024 | Conference Paper |

Mixed Attention SAR Ship Recognition Network with Robust Background Interference

Yanyu Lyu;Yuanzhe Shang;Chongsong Wang;Yulin Huang;Jifang Pei;Wei Pu;Weibo Huo;Junjie Wu;Yin Zhang

IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium
Year: 2024 | Conference Paper |
Ship recognition in synthetic aperture radar (SAR) images is a significant and fundamental step in the maritime surveillance. However, recognition of ships inevitably faces background interference in the maritime environment. The interference guides the network focusing on useless even harmful regions. To deal with issue, a mixed attention mechanism consists of coordinate and Squeeze-and-Excitatio...Show More

Mixed Attention SAR Ship Recognition Network with Robust Background Interference

Yanyu Lyu;Yuanzhe Shang;Chongsong Wang;Yulin Huang;Jifang Pei;Wei Pu;Weibo Huo;Junjie Wu;Yin Zhang

Year: 2024 | Conference Paper |

A SAR Open-Set Recognition Method Aided by Hierarchically Reconstructive Latent Representation Learning

Yuchun Lu;Jifang Pei;Yin Zhang;Weibo Huo;Yulin Huang;Wenjing Wang;Jianyu Yang

IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium
Year: 2024 | Conference Paper |
Cited by: Papers (1)
Automatic target recognition (ATR) based on synthetic aperture radar (SAR) images has already obtained remarkable achievements on closed-set task. However, the recognition in a real-world scenario should not only identify the known classes but also appropriately deal with the unknown ones. To this end, we propose a SAR open-set recognition method aided by hierarchically reconstructive latent repre...Show More

A SAR Open-Set Recognition Method Aided by Hierarchically Reconstructive Latent Representation Learning

Yuchun Lu;Jifang Pei;Yin Zhang;Weibo Huo;Yulin Huang;Wenjing Wang;Jianyu Yang

Year: 2024 | Conference Paper |

Beta Mixture Model and Boundary Amplification Guided Label Noise Mitigation for Polsar Image Classification

Xiaowei Lin;Yanjing Ma;Jifang Pei;Weibo Huo;Junjie Wu;Yin Zhang;Yulin Huang;Jianyu Yang

IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium
Year: 2024 | Conference Paper |
In the field of polarimetric synthetic aperture radar (PolSAR) automatic target classification (ATR), convolutional neural network (CNN) based methods have excelled owing to their adept feature extraction capabilities. However, these methods heavily rely on a sufficiently labeled training dataset for superior classification performance. Limited PolSAR training samples and inevitable noisy labels o...Show More

Beta Mixture Model and Boundary Amplification Guided Label Noise Mitigation for Polsar Image Classification

Xiaowei Lin;Yanjing Ma;Jifang Pei;Weibo Huo;Junjie Wu;Yin Zhang;Yulin Huang;Jianyu Yang

Year: 2024 | Conference Paper |

A DCT-Based Local Contrast Enhancement SAR Imaging Detection Algorithm*

Ran An;Weibo Huo;Yujie Zhang;Jifang Pei;Yin Zhang;Yulin Huang

IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium
Year: 2024 | Conference Paper |
Synthetic aperture radar (SAR) is commonly used for ship imaging on the sea. By using small target detection algorithms, ship targets can be highlighted under different SAR backgrounds for detection and observation. Local contrast measurement (LCM) has poor detection performance in situations with strong background noise or uneven distribution, and its results cannot preserve the shape features of...Show More

A DCT-Based Local Contrast Enhancement SAR Imaging Detection Algorithm*

Ran An;Weibo Huo;Yujie Zhang;Jifang Pei;Yin Zhang;Yulin Huang

Year: 2024 | Conference Paper |

Angular Superresolution for Forward-Looking Scanning Radar With Pulse Interference Using Cross-Domain Low-Rank and Sparse Optimization

Deqing Mao;Jianyu Yang;Xingyu Tuo;Yongchao Zhang;Weibo Huo;Yin Zhang;Yulin Huang

IEEE Transactions on Geoscience and Remote Sensing
Year: 2024 | Volume: 62 | Journal Article |
Cited by: Papers (1)
Frequency modulation continuous wave (FMCW) radar has been paid much attention in forward-looking navigation applications because of its no-blind-range capability. However, after dechirp processing, pulse interference signals may appear in the range time domain, which seriously pollutes the whole radiation direction. In this article, a cross-domain low-rank and sparse (CD-LRS) optimization framewo...Show More

Angular Superresolution for Forward-Looking Scanning Radar With Pulse Interference Using Cross-Domain Low-Rank and Sparse Optimization

Deqing Mao;Jianyu Yang;Xingyu Tuo;Yongchao Zhang;Weibo Huo;Yin Zhang;Yulin Huang

Year: 2024 | Volume: 62 | Journal Article |

Deinterleaving Pulse Trains via RPMA-TConv for Parameter-Agile Radar

Caiyu Liang;Han Tang;Weibo Huo;Yin Zhang;Jifang Pei;Yulin Huang

2024 IEEE Radar Conference (RadarConf24)
Year: 2024 | Conference Paper |
Radar pulse trains deinterleaving is a challenging task in modern electronic reconnaissance. The RPMA-TConv model based on multi-branch atrous convolution and feature reconstruction is proposed to solve the problem of deinterleaving parameter-agile emitters. The time of arrival (TOA), center frequency (CF) and pulse width (PW) are used to characterize the relative position and variation pattern of...Show More

Deinterleaving Pulse Trains via RPMA-TConv for Parameter-Agile Radar

Caiyu Liang;Han Tang;Weibo Huo;Yin Zhang;Jifang Pei;Yulin Huang

Year: 2024 | Conference Paper |

A Split SPICE-TV Super-Resolution Method for Scanning Radar

Jiawei Luo;Yongchao Zhang;Tianzhi Sun;Yin Zhang;Weibo Huo;Yulin Huang;Jianyu Yang

2024 IEEE Radar Conference (RadarConf24)
Year: 2024 | Conference Paper |
Recently, the sparse iterative covariance fitting estimation (SPICE) method has garnered attention for scanning radar super-resolution imaging. While it offers improved azimuth resolution, a notable drawback is its limited efficacy in reconstructing target contours. In this paper, a Split SPICE total variation (Split SPICE-TV) super-resolution method is proposed to address this problem. First, the...Show More

A Split SPICE-TV Super-Resolution Method for Scanning Radar

Jiawei Luo;Yongchao Zhang;Tianzhi Sun;Yin Zhang;Weibo Huo;Yulin Huang;Jianyu Yang

Year: 2024 | Conference Paper |

Intra-Pulse Modulation Recognition Method of Radar Signals Based on GFSE-InRes2Net Under Multipath Effects

Yang Luo;Yujie Zhang;Weibo Huo;Jifang Pei;Yin Zhang;Yulin Huang;Caiyu Liang

2024 IEEE Radar Conference (RadarConf24)
Year: 2024 | Conference Paper |
Cited by: Papers (1)
In modern complex electromagnetic environments, the received radar signals are often affected by multipath effects. The distortion and noise of the multipath signals make it difficult to recognize the intra-pulse modulation of radar signals. To address this issue, a gram angular field-fusion Inception-Res2Net with squeeze-and-excitation (GFSE-InRes2Net) is proposed. Firstly, in addition to the con...Show More

Intra-Pulse Modulation Recognition Method of Radar Signals Based on GFSE-InRes2Net Under Multipath Effects

Yang Luo;Yujie Zhang;Weibo Huo;Jifang Pei;Yin Zhang;Yulin Huang;Caiyu Liang

Year: 2024 | Conference Paper |

An Efficient Waveform Design Method with Spectral Coexistence and Pulse-like Autocorrelation Function

Cui Zhang;Jifang Pei;Yujie Zhang;Yin Zhang;Weibo Huo;Yulin Huang;Jianyu Yang

2024 IEEE Radar Conference (RadarConf24)
Year: 2024 | Conference Paper |
Spectrum coexistence capability is particularly important for radar systems in complex electromagnetic environments, since it can suppress interference arising from signals in the common frequency band. In this paper, a unimodular waveform design method based on the Karush-Kuhn-Tucker (KKT) conditions is proposed to design transmit waveforms with spectral coexistence and pulse-like autocorrelation...Show More

An Efficient Waveform Design Method with Spectral Coexistence and Pulse-like Autocorrelation Function

Cui Zhang;Jifang Pei;Yujie Zhang;Yin Zhang;Weibo Huo;Yulin Huang;Jianyu Yang

Year: 2024 | Conference Paper |

Anti-Jamming Strategy Design Based on Deep Q-Network for Slope-Varying LFM Signal

Shubo Chen;Weibo Huo;Cui Zhang;Jifang Pei;Yin Zhang;Yulin Huang;Jiahao Shen

2024 IEEE Radar Conference (RadarConf24)
Year: 2024 | Conference Paper |
Radar systems with anti-jamming capability have become extremely important in the complex electromagnetic environment. In this paper, an anti-jamming strategy generation method based on deep Q-network (DQN) is proposed for slope-varying linear frequency modulation (SV-LFM) signals. First, the radar-jammer confrontation scenario is modeled as a Markov decision process. Subsequently, a reward functi...Show More

Anti-Jamming Strategy Design Based on Deep Q-Network for Slope-Varying LFM Signal

Shubo Chen;Weibo Huo;Cui Zhang;Jifang Pei;Yin Zhang;Yulin Huang;Jiahao Shen

Year: 2024 | Conference Paper |

Class Ambiguity Regularized Adversarial Domain Adaptation for Cross-resolution SAR ATR

Yuchun Lu;Jifang Pei;Yi Liu;Yin Zhang;Weibo Huo;Yulin Huang;Jianyu Yang;Jianqi Wu

2024 IEEE Radar Conference (RadarConf24)
Year: 2024 | Conference Paper |
Automatic target recognition (ATR) plays a critical role in synthetic aperture radar (SAR) applications. However, existing SAR ATR methods usually assume that the training and test SAR samples have the same imaging resolution but it is hard to satisfy in practice due to the complexity of the SAR imaging process, which results in the discrepancy between different resolution domains and degrades the...Show More

Class Ambiguity Regularized Adversarial Domain Adaptation for Cross-resolution SAR ATR

Yuchun Lu;Jifang Pei;Yi Liu;Yin Zhang;Weibo Huo;Yulin Huang;Jianyu Yang;Jianqi Wu

Year: 2024 | Conference Paper |

A Dual-Mode Radar Signal Sorting Method Based on Adaptive Density Peak Clustering

Aijiong Yang;Weibo Huo;Gengchen Xu;Yujie Zhang;Yulin Huang;Jifang Pei;Yin Zhang;Yi Liu

2024 IEEE Radar Conference (RadarConf24)
Year: 2024 | Conference Paper |
Radar signal sorting is a key component in electronic support measures (ESM). Adaptive sorting algorithms are a type of method that does not require prior understanding of radar information in the sorting scenario. However, these methods have consistently faced the issue of high runtime costs. To address this problem, in this paper, a dual-mode radar signal sorting method based on density clusteri...Show More

A Dual-Mode Radar Signal Sorting Method Based on Adaptive Density Peak Clustering

Aijiong Yang;Weibo Huo;Gengchen Xu;Yujie Zhang;Yulin Huang;Jifang Pei;Yin Zhang;Yi Liu

Year: 2024 | Conference Paper |

Dynamically Weighted Prototypical Learning Method for Few-Shot SAR ATR

Congwen Wu;Jianyu Yang;Yuanzhe Shang;Jifang Pei;Deqing Mao;Weibo Huo;Yulin Huang;Yin Zhang

IEEE Geoscience and Remote Sensing Letters
Year: 2024 | Volume: 21 | Journal Article |
Cited by: Papers (4)
Automatic target recognition (ATR) holds a crucial position in synthetic aperture radar (SAR) image interpretation. Despite deep-learning advancements having significantly propelled SAR ATR, addressing the challenge of target recognition with a few training data remains a vital concern in SAR applications. Two main issues still exist: 1) in few-shot SAR ATR, the depth and width of convolutional ne...Show More

Dynamically Weighted Prototypical Learning Method for Few-Shot SAR ATR

Congwen Wu;Jianyu Yang;Yuanzhe Shang;Jifang Pei;Deqing Mao;Weibo Huo;Yulin Huang;Yin Zhang

Year: 2024 | Volume: 21 | Journal Article |

Parameter Extraction Method of Overlapping Radar Signals Using Modulation Recognition-Guided Semantic Segmentation

Weibo Huo;Yang Luo;Hao Wang;Jifang Pei;Yin Zhang;Yulin Huang;Jianyu Yang

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Year: 2024 | Volume: 17 | Journal Article |
Parameter extraction of radar signals is an important but challenging task in electronic warfare. In the modern electromagnetic environment, the radiation sources greatly increase, causing different radar signals to overlap, making the parameter extraction of radar signals difficult. Meanwhile, using radar signal parameter extraction methods that are not suitable for dealing with overlapping signa...Show More

Parameter Extraction Method of Overlapping Radar Signals Using Modulation Recognition-Guided Semantic Segmentation

Weibo Huo;Yang Luo;Hao Wang;Jifang Pei;Yin Zhang;Yulin Huang;Jianyu Yang

Year: 2024 | Volume: 17 | Journal Article |

Transmit Beampattern Design with Similarity and Variable Modulus Constraints for MIMO Radar

Cui Zhang;Jifang Pei;Yujie Zhang;Qingying Yi;Weibo Huo;Yin Zhang;Yulin Huang

IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium
Year: 2023 | Conference Paper |
In this paper, the constrained waveform design of multiple-input multiple-output (MIMO) radar is considered to achieve transmit beampattern assignment. Firstly, we construct a framework that minimizes the spatial integrated sidelobe level ratio (ISLR) as the objective function and constrains the transmit waveforms in terms of amplitude fluctuations and similarity. To solve the resulting non-convex...Show More

Transmit Beampattern Design with Similarity and Variable Modulus Constraints for MIMO Radar

Cui Zhang;Jifang Pei;Yujie Zhang;Qingying Yi;Weibo Huo;Yin Zhang;Yulin Huang

Year: 2023 | Conference Paper |

Transfer Learning on Self-Supervised Model for SAR Target Recognition with Limited Labeled Data

Xiaoyu Liu;Lin Liu;Chenwei Wang;Jifang Pei;Weibo Huo;Yin Zhang;Yulin Huang

IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium
Year: 2023 | Conference Paper |
Deep learning contributes to significant improvements in synthetic aperture radar (SAR) target recognition performance. Most SAR target recognition methods are based on supervised learning and require labeled SAR data. There only exists limited labeled data due to the time-consuming and laborious work of labeling, and there is still a large amount of available unlabeled radar data. Therefore, we a...Show More

Transfer Learning on Self-Supervised Model for SAR Target Recognition with Limited Labeled Data

Xiaoyu Liu;Lin Liu;Chenwei Wang;Jifang Pei;Weibo Huo;Yin Zhang;Yulin Huang

Year: 2023 | Conference Paper |

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