Songchang Jin - IEEE Xplore Author Profile
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Songchang Jin

Publications
16
Citations
18
Publications by Year
20192025
Co-Authors:
Show All Co-Authors (38)
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Songchang Jin

Affiliation

Academy of Military Sciences, Beijing, China

Publication Topics

Biography

Songchang Jin received the B.S. degree from the Department of Automatic, Tsinghua University, Beijing, China, in 2008, and the M.S. and Ph.D. degrees from the Department of Computer, National University of Defense Technology, Changsha, China, in 2010 and 2015, respectively.,He is currently an Associate Professor with the Intelligent Game and Decision Laboratory (IGDL), Academy of Military Sciences, Beijing, China. His research interests include big data, with a particular interest in operating system.(Based on document published on 29 January 2024).
Publications
16
Citations
18
Publications by Year
20192025
Co-Authors:
Show All Co-Authors (38)
Author's Published Works

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Multi-Agent Hierarchical Graph Attention Actor-Critic Reinforcement Learning

Tongyue Li;Dianxi Shi;Songchang Jin;Zhen Wang;Huanhuan Yang;Yang Chen

ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2025 | Conference Paper |
Multi-agent systems often face challenges such as elevated communication demands and intricate interactions. We propose an innovative hierarchical graph attention actor-critic reinforcement learning method to address the issues, which uses the hierarchical graph attention to capture the relationships of cooperation or competition among agents, and the agent enables a better understand of the dynam...Show More

Multi-Agent Hierarchical Graph Attention Actor-Critic Reinforcement Learning

Tongyue Li;Dianxi Shi;Songchang Jin;Zhen Wang;Huanhuan Yang;Yang Chen

Year: 2025 | Conference Paper |

Hierarchy Coverage Path Planning With Proactive Extremum Prevention in Unknown Environments

Lin Li;Dianxi Shi;Songchang Jin;Xing Zhou;Yahui Li;Bin Bai

IEEE Robotics and Automation Letters
Year: 2025 | Volume: 10, Issue: 4 | Journal Article |
The local extremum is a crucial factor that affects the efficiency of online coverage path planning (CPP). Most online CPP methods generate coverage motions point by point in unknown environments. However, these solutions ignore efficient global coverage and probably result in local extremum. This letter presents a hierarchy coverage path planning approach (HCPP) with proactive extremum prevention...Show More

Hierarchy Coverage Path Planning With Proactive Extremum Prevention in Unknown Environments

Lin Li;Dianxi Shi;Songchang Jin;Xing Zhou;Yahui Li;Bin Bai

Year: 2025 | Volume: 10, Issue: 4 | Journal Article |

A Squint Imaging Algorithm for the Multireceiver SAS with High Mapping Rate

Mengbo Ma;Songchang Jin;Ruihao Li;Wei Yi

2024 IEEE International Conference on Signal, Information and Data Processing (ICSIDP)
Year: 2024 | Conference Paper |
In squint multireceiver synthetic aperture sonar (SAS), the platform moves continuously along the azimuth direction during the interpulse and intrapulse. The intrapulse motion results in Doppler scale effect (DSE) of the echo signal, and the squint angle will exacerbate this effect. Considering the intrapulse motion of the sonar platform, we derived the time-varying time delay and range history, a...Show More

A Squint Imaging Algorithm for the Multireceiver SAS with High Mapping Rate

Mengbo Ma;Songchang Jin;Ruihao Li;Wei Yi

Year: 2024 | Conference Paper |

A CS Algorithm for Multireceiver SAS with Angular Motion Error Compensation

Mengbo Ma;Songchang Jin;Ruihao Li

2024 IEEE International Conference on Signal, Information and Data Processing (ICSIDP)
Year: 2024 | Conference Paper |
Motion is both the answer and the question, which is the perfect interpretation of synthetic aperture imaging. In multireceiver synthetic aperture sonar (SAS), the angular motion error of SAS platform leads to different sway and heave errors for each receiver, resulting in squint, which will have a significant impact on image quality. Considering the angular motion of the multireceiver SAS, we est...Show More

A CS Algorithm for Multireceiver SAS with Angular Motion Error Compensation

Mengbo Ma;Songchang Jin;Ruihao Li

Year: 2024 | Conference Paper |

ICF-Loc: An Infrared-Based Coarse-to-Fine Approach for UAV Visual Geolocation under GPS-Denied Environments

Zhen Wang;Dianxi Shi;Chunping Qiu;Songchang Jin;Tongyue Li;Yanyan Shi

2024 IEEE International Conference on Multimedia and Expo (ICME)
Year: 2024 | Conference Paper |
Visual geolocation plays a crucial role when GPS is unavailable in the Unmanned Aerial Vehicles (UAVs). Many methods rely on visible light cameras, which may not perform well in low-light or foggy conditions. To address this issue, we propose an advanced UAV visual geolocation method called ICF-Loc, which utilizes infrared images in a coarse-to-fine approach. ICF-Loc consists of two stages: a retr...Show More

ICF-Loc: An Infrared-Based Coarse-to-Fine Approach for UAV Visual Geolocation under GPS-Denied Environments

Zhen Wang;Dianxi Shi;Chunping Qiu;Songchang Jin;Tongyue Li;Yanyan Shi

Year: 2024 | Conference Paper |

SVR-AVT: Scale Variation Robust Active Visual Tracking

Biao Zhang;Songchang Jin;Qianying Ouyang;Huanhuan Yang;Yuxi Zheng;Chunlian Fu;Dianxi Shi

2024 International Joint Conference on Neural Networks (IJCNN)
Year: 2024 | Conference Paper |
Active Visual Tracking (AVT) is a significant research area with extensive applications in fields such as drones and autonomous driving. AVT involves controlling camera motion based on visual observations to track target object(s). In dynamic environments, especially with the presence of distractors, AVT faces the challenge of scale variation. Existing methods struggle to effectively handle these ...Show More

SVR-AVT: Scale Variation Robust Active Visual Tracking

Biao Zhang;Songchang Jin;Qianying Ouyang;Huanhuan Yang;Yuxi Zheng;Chunlian Fu;Dianxi Shi

Year: 2024 | Conference Paper |

Improved Communication and Collision-Avoidance in Dynamic Multi-Agent Path Finding

Jing Xie;Yongjun Zhang;Qianying Ouyang;Huanhuan Yang;Fang Dong;Dianxi Shi;Songchang Jin

2024 International Joint Conference on Neural Networks (IJCNN)
Year: 2024 | Conference Paper |
Multi-Agent Path Finding (MAPF) is a classic problem with a wide range of applications. To cope with more complex situations in reality, Dynamic MAPF (DMAPF) has received much attention. The existing DMAPF definition lacks completeness or considers too simple situations. In this paper, we comprehensively model DMAPF based on realistic scenarios. Consequently, dynamic scenarios bring many problems....Show More

Improved Communication and Collision-Avoidance in Dynamic Multi-Agent Path Finding

Jing Xie;Yongjun Zhang;Qianying Ouyang;Huanhuan Yang;Fang Dong;Dianxi Shi;Songchang Jin

Year: 2024 | Conference Paper |

Crowd Perception Communication-Based Multi- Agent Path Finding With Imitation Learning

Jing Xie;Yongjun Zhang;Huanhuan Yang;Qianying Ouyang;Fang Dong;Xinyu Guo;Songchang Jin;Dianxi Shi

IEEE Robotics and Automation Letters
Year: 2024 | Volume: 9, Issue: 10 | Journal Article |
Deep reinforcement learning-based Multi-Agent Path Finding (MAPF) has gained significant attention due to its remarkable adaptability to environments. Existing methods primarily leverage multi-agent communication in a fully-decentralized framework to maintain scalability while enhancing information exchange among agents. However, as the number of agents and obstacles increases, the environment bec...Show More

Crowd Perception Communication-Based Multi- Agent Path Finding With Imitation Learning

Jing Xie;Yongjun Zhang;Huanhuan Yang;Qianying Ouyang;Fang Dong;Xinyu Guo;Songchang Jin;Dianxi Shi

Year: 2024 | Volume: 9, Issue: 10 | Journal Article |

Sequence Matching for Image-Based UAV-to-Satellite Geolocalization

Zhen Wang;Dianxi Shi;Chunping Qiu;Songchang Jin;Tongyue Li;Yanyan Shi;Zhe Liu;Ziteng Qiao

IEEE Transactions on Geoscience and Remote Sensing
Year: 2024 | Volume: 62 | Journal Article |
Cited by: Papers (3)
Unmanned aerial vehicle (UAV)-to-satellite geolocalization offers accurate drift-free navigation in the absence of external positioning signals. Increased deep-learning-based approaches have demonstrated their potential for high accuracy by framing the problem as a one-to-all retrieval task. However, in real-world scenario, the problem is not just a one-to-all retrieval task, which leads to a gap ...Show More

Sequence Matching for Image-Based UAV-to-Satellite Geolocalization

Zhen Wang;Dianxi Shi;Chunping Qiu;Songchang Jin;Tongyue Li;Yanyan Shi;Zhe Liu;Ziteng Qiao

Year: 2024 | Volume: 62 | Journal Article |

Spatial-Aware Dynamic Lightweight Self-Supervised Monocular Depth Estimation

Linna Song;Dianxi Shi;Jianqiang Xia;Qianying Ouyang;Ziteng Qiao;Songchang Jin;Shaowu Yang

IEEE Robotics and Automation Letters
Year: 2024 | Volume: 9, Issue: 1 | Journal Article |
Cited by: Papers (1)
Self-supervised monocular depth estimation has attracted extensive attention in recent years. Lightweight depth estimation methods are crucial for resource-constrained edge devices. However, existing lightweight methods often encounter the challenge of limited representation capacity and increased computational resource consumption for image reconstruction. To alleviate these issues, we propose a ...Show More

Spatial-Aware Dynamic Lightweight Self-Supervised Monocular Depth Estimation

Linna Song;Dianxi Shi;Jianqiang Xia;Qianying Ouyang;Ziteng Qiao;Songchang Jin;Shaowu Yang

Year: 2024 | Volume: 9, Issue: 1 | Journal Article |

End-to-End Binocular Active Visual Tracking Based on Reinforcement Learning

Biao Zhang;Songchang Jin;Shaowu Yang;Qianying Ouyang;Yuxi Zheng;Dianxi Shi

2023 2nd International Conference on Machine Learning, Cloud Computing and Intelligent Mining (MLCCIM)
Year: 2023 | Conference Paper |
Active Visual Tracking (AVT) aims at controlling the motion system of the tracker to follow the target given visual observations. Existing works have achieved significant breakthroughs by leveraging deep reinforcement learning. However, these methods rely solely on a single visual observation image, and the tracker can only infer relationship between the position of itself and the target according...Show More

End-to-End Binocular Active Visual Tracking Based on Reinforcement Learning

Biao Zhang;Songchang Jin;Shaowu Yang;Qianying Ouyang;Yuxi Zheng;Dianxi Shi

Year: 2023 | Conference Paper |

Enhancing Offline Coverage Path Planning with Deep Reinforcement Learning

Yaoning Lian;Shaowu Yang;Songchang Jin;Yuxi Zheng;Dianxi Shi

2023 2nd International Conference on Machine Learning, Cloud Computing and Intelligent Mining (MLCCIM)
Year: 2023 | Conference Paper |
Coverage path planning is a critical challenge in various domains, including drones, robots, and automation devices. Traditional methods heavily rely on manually crafted heuristic rules, which struggle to adapt to complex environments and task requirements. This paper proposes a deep reinforcement learning-based algorithm for coverage path planning to achieve automated and intelligent solutions. T...Show More

Enhancing Offline Coverage Path Planning with Deep Reinforcement Learning

Yaoning Lian;Shaowu Yang;Songchang Jin;Yuxi Zheng;Dianxi Shi

Year: 2023 | Conference Paper |

Collision-free Coverage Path Planning for the Variable-speed Curvature-constrained Robot

Lin Li;Dianxi Shi;Songchang Jin;Yixuan Sun;Xing Zhou;Shaowu Yang;Hengzhu Liu

2023 IEEE International Conference on Robotics and Automation (ICRA)
Year: 2023 | Conference Paper |
Cited by: Papers (2)
Dubins coverage has been extensively researched to address the coverage path planning (CPP) problem of a known environment for the curvature-constrained robot. However, its fixed-speed assumption prevents the robot from accelerating to reduce the time and limits its flexibility to avoid obstacles. Therefore, this paper presents a collision-free CPP approach (CFC) for the obstacle-constrained envir...Show More

Collision-free Coverage Path Planning for the Variable-speed Curvature-constrained Robot

Lin Li;Dianxi Shi;Songchang Jin;Yixuan Sun;Xing Zhou;Shaowu Yang;Hengzhu Liu

Year: 2023 | Conference Paper |

Dual-template Siamese Network with Cross-Correlation Fusion for Tracking

Ning Wang;Dianxi Shi;Ying Kang;Zunlin Fan;Songchang Jin;Yongjun Zhang

2020 IEEE 32nd International Conference on Tools with Artificial Intelligence (ICTAI)
Year: 2020 | Conference Paper |
Cited by: Papers (3)
Siamese network based trackers treat tracking as a maximum matching between the detection region and the template, in which the template is either fixed or updated. The template-fixed trackers degrade accuracy since the appearance variations of the object in the subsequent frames are lacked; while the template-updated trackers depress robustness once the tracking drift occurs. In this paper, we ap...Show More

Dual-template Siamese Network with Cross-Correlation Fusion for Tracking

Ning Wang;Dianxi Shi;Ying Kang;Zunlin Fan;Songchang Jin;Yongjun Zhang

Year: 2020 | Conference Paper |

Balanced Multi-Region Coverage Path Planning for Unmanned Aerial Vehicles

Xiaoxiao Yu;Songchang Jin;Dianxi Shi;Lin Li;Ying Kang;Junbo Zou

2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC)
Year: 2020 | Conference Paper |
Cited by: Papers (9)
Nowadays, Unmanned Aerial Vehicles (UAVs) are playing increasingly important roles in agriculture, rescuing and surveillance due to their small size, low cost and strong adaptability. Coverage Path Planning (CPP) is a fundamental problem for UAV applications, which means to find a path covering all the targets or regions of interest. Researches on CPP in a single region have been studied for decad...Show More

Balanced Multi-Region Coverage Path Planning for Unmanned Aerial Vehicles

Xiaoxiao Yu;Songchang Jin;Dianxi Shi;Lin Li;Ying Kang;Junbo Zou

Year: 2020 | Conference Paper |

Learning to Track by Bi-Directional Long Short-Term Memory Networks

Chen Pan;Dianxi Shi;Naiyang Guan;Yongjun Zhang;Liujing Wang;Songchang Jin

2019 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI)
Year: 2019 | Conference Paper |
Invisualtracking, deeplearningbasedtrackershave demonstrated strong competitiveness under many challenging conditions such as background clutters and occlusions. Several recent studies have devoted to defeating such conditions by using Recurrent Neural Networks (RNNs) to explore temporal smoothness and stable deep representation. In this paper, we propose a tracker based on Bi-directional Long Sho...Show More

Learning to Track by Bi-Directional Long Short-Term Memory Networks

Chen Pan;Dianxi Shi;Naiyang Guan;Yongjun Zhang;Liujing Wang;Songchang Jin

Year: 2019 | Conference Paper |

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