Unmanned Aerial Vehicle(UAVs) swarm has great advantage over traditional equipment in cooperative detection scenario for its easy-maneuverability, no human injury and low cost, etc. As a representative task in cooperative detection, region coverage has widely applications in environmental monitoring, search and rescue, etc. In UAV cooperative detection tasks, the most critical step is task planning, which has direct impact on the overall detection performance. The target of task planning is to generate planned actions and flight route for UAVs to complete specific detection task according to UAV swarm locations, sensor ability, task region, etc. However, traditional task planning methods for UAV cooperative detection that based on evolutionary computing or reinforcement learning always need plenty of time for getting planning results. In this paper, we proposed a top-down task planning algorithm based on greedy policy to tackle this problem. The core idea of the proposed method lies in that we choose optimal detection trace from all trace candidates during each planning step in a greedy manner via a predefined performance indicator. Moreover, we also proposed a simple but effective procedure for generate detection trace candidates by corner points and nearest border points extraction. To evaluate the effectiveness of the proposed method, we conducted comprehensive experiments for the representative swarm detection task region coverage. Experiment results demonstrated the effectiveness of the proposed method and superiority over traditional methods on task planning speed.
{"title":"Automatic UAV Swarm Task Planning in Cooperative Region Coverage Detection based on Greedy Policy","authors":"Rentuo Tao, Shikang Li, Xianzhe Xu, Yawei Chen, Linghao Xia, Yuhao Yang","doi":"10.1109/ICUS55513.2022.9986918","DOIUrl":"https://doi.org/10.1109/ICUS55513.2022.9986918","url":null,"abstract":"Unmanned Aerial Vehicle(UAVs) swarm has great advantage over traditional equipment in cooperative detection scenario for its easy-maneuverability, no human injury and low cost, etc. As a representative task in cooperative detection, region coverage has widely applications in environmental monitoring, search and rescue, etc. In UAV cooperative detection tasks, the most critical step is task planning, which has direct impact on the overall detection performance. The target of task planning is to generate planned actions and flight route for UAVs to complete specific detection task according to UAV swarm locations, sensor ability, task region, etc. However, traditional task planning methods for UAV cooperative detection that based on evolutionary computing or reinforcement learning always need plenty of time for getting planning results. In this paper, we proposed a top-down task planning algorithm based on greedy policy to tackle this problem. The core idea of the proposed method lies in that we choose optimal detection trace from all trace candidates during each planning step in a greedy manner via a predefined performance indicator. Moreover, we also proposed a simple but effective procedure for generate detection trace candidates by corner points and nearest border points extraction. To evaluate the effectiveness of the proposed method, we conducted comprehensive experiments for the representative swarm detection task region coverage. Experiment results demonstrated the effectiveness of the proposed method and superiority over traditional methods on task planning speed.","PeriodicalId":345773,"journal":{"name":"2022 IEEE International Conference on Unmanned Systems (ICUS)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127230607","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-28DOI: 10.1109/ICUS55513.2022.9987068
J. Xiong, Juan Li, Jie Li
When employing swarms of unmanned aerial vehicles (UAVs) in communication-constrained environments, it is of vital importance to coordinate their actions in cooperation despite sparse and unreliable communication channels. This paper proposes an adaptive dual-phased threshold-based assignment scheme for robust coordination under lossy communication. The assignment scheme is inspired by features in handshake protocols, using records upon failed communications to keep track of swarm mates and consensus rates. Resendings of vital information pieces within core steps of assignment negotiation are arranged to increase consensus rates above the threshold. The resendings are constrained by switching criteria designed to balance between information integrity and assignment timeliness. The overall scheme is termed Robust Assignment under Lossy Communication (RALC). The proposed RALC is evaluated at various levels of communication reliability, using the Bernoulli model and the Gilbert-Elliott model. Numerical experiments demonstrate superior performance of the proposed RALC against the Consensus-Based Auction Algorithm (CBAA), the Probability- Tuned Market-based Allocation (PTMA), and the Repeated G- Prim auction (RGPrim) in communication degraded scenarios.
{"title":"Adaptive Assignment Re-Consensus in Communication-Constrained Environments","authors":"J. Xiong, Juan Li, Jie Li","doi":"10.1109/ICUS55513.2022.9987068","DOIUrl":"https://doi.org/10.1109/ICUS55513.2022.9987068","url":null,"abstract":"When employing swarms of unmanned aerial vehicles (UAVs) in communication-constrained environments, it is of vital importance to coordinate their actions in cooperation despite sparse and unreliable communication channels. This paper proposes an adaptive dual-phased threshold-based assignment scheme for robust coordination under lossy communication. The assignment scheme is inspired by features in handshake protocols, using records upon failed communications to keep track of swarm mates and consensus rates. Resendings of vital information pieces within core steps of assignment negotiation are arranged to increase consensus rates above the threshold. The resendings are constrained by switching criteria designed to balance between information integrity and assignment timeliness. The overall scheme is termed Robust Assignment under Lossy Communication (RALC). The proposed RALC is evaluated at various levels of communication reliability, using the Bernoulli model and the Gilbert-Elliott model. Numerical experiments demonstrate superior performance of the proposed RALC against the Consensus-Based Auction Algorithm (CBAA), the Probability- Tuned Market-based Allocation (PTMA), and the Repeated G- Prim auction (RGPrim) in communication degraded scenarios.","PeriodicalId":345773,"journal":{"name":"2022 IEEE International Conference on Unmanned Systems (ICUS)","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132836262","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-28DOI: 10.1109/ICUS55513.2022.9987037
Jihao Cai, Guoxin Li
This paper studies an uplink network in which a hovering unmanned aerial vehicle (UAV) serves as a flying base station and multiple ground users access different resource blocks (RBs) with the aid of power-domain non-orthogonal multiple access (NOMA). We aim to maximize the sum of information rate of the network through appropriate UAV placement and RB allocation. The mixed integer nonconvex problem is decomposed into two layers. The inner layer, RB allocation given the position of the UAV, is solved by hill-climbing. The outer layer, UAV placement given the result of RB allocation of the inner layer, is solved by particle swarm optimization. Simulation results show that the proposed layered scheme outperforms existing resource allocation strategies.
{"title":"UAV-assisted Uplink NOMA Networks: UAV Placement and Resource Block Allocation","authors":"Jihao Cai, Guoxin Li","doi":"10.1109/ICUS55513.2022.9987037","DOIUrl":"https://doi.org/10.1109/ICUS55513.2022.9987037","url":null,"abstract":"This paper studies an uplink network in which a hovering unmanned aerial vehicle (UAV) serves as a flying base station and multiple ground users access different resource blocks (RBs) with the aid of power-domain non-orthogonal multiple access (NOMA). We aim to maximize the sum of information rate of the network through appropriate UAV placement and RB allocation. The mixed integer nonconvex problem is decomposed into two layers. The inner layer, RB allocation given the position of the UAV, is solved by hill-climbing. The outer layer, UAV placement given the result of RB allocation of the inner layer, is solved by particle swarm optimization. Simulation results show that the proposed layered scheme outperforms existing resource allocation strategies.","PeriodicalId":345773,"journal":{"name":"2022 IEEE International Conference on Unmanned Systems (ICUS)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134007016","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Based on the decision-making architecture of information pooling and sharing in the hidden layer, the communication protocol is set manually, and the pooling method is used to integrate the information. Although the problem of communication and extension between agents is solved, it is difficult for tasks lacking prior knowledge to design effective communication protocols. The centralized decision- making architecture based on two-way RNN communication uses the information storage characteristics of two-way RNN structure. It can self learn the communication protocol between agents, which overcomes the rigid requirement of task prior knowledge in communication protocol design. The action distribution of a single agent is used as the output of the multi- agent network to replace the joint action distribution, and the global state information in the environment is used as the input instead of simply inputting the local information to different agents. The effectiveness of the method is verified by an example.
{"title":"Decision-making Method Based on Multi-agent Deep Reinforcement Learning","authors":"Weiwei Bian, Chunguang Wang, Chan Liu, Kuihua Huang, Ying Mi, Yanxiang Jia","doi":"10.1109/ICUS55513.2022.9987201","DOIUrl":"https://doi.org/10.1109/ICUS55513.2022.9987201","url":null,"abstract":"Based on the decision-making architecture of information pooling and sharing in the hidden layer, the communication protocol is set manually, and the pooling method is used to integrate the information. Although the problem of communication and extension between agents is solved, it is difficult for tasks lacking prior knowledge to design effective communication protocols. The centralized decision- making architecture based on two-way RNN communication uses the information storage characteristics of two-way RNN structure. It can self learn the communication protocol between agents, which overcomes the rigid requirement of task prior knowledge in communication protocol design. The action distribution of a single agent is used as the output of the multi- agent network to replace the joint action distribution, and the global state information in the environment is used as the input instead of simply inputting the local information to different agents. The effectiveness of the method is verified by an example.","PeriodicalId":345773,"journal":{"name":"2022 IEEE International Conference on Unmanned Systems (ICUS)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132812593","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper, we present a complete LiDAR SLAM framework, SCL-SLAM, by integrating the loop closure module with the Scan Context descriptor into the tightly-coupled LiDAR-Inertial odometry FAST-LIO2. As a front-end, the direct LiDAR-Inertial odometry module efficiently and robustly produces motion estimates and undistorted scans. Toward the global localization based on 3D LiDAR scans, the lightweight Scan Context descriptor is used in the loop detection module. Additionally, the scan input is filtered through the keyframe selection module to improve the computation efficiency. As a back-end, a pose graph optimization is performed for the optimized trajectory and globally consistent map. SCL-SLAM is extensively evaluated on public datasets and a robot platform over various scales and environments. Experimental result shows that SCL-SLAM achieves higher accuracy than other state-of-art LiDAR SLAM systems and real-time performance. We also extend the proposed system to a centralized architecture SLAM framework for the robot team to use with 3D LiDAR observations.
{"title":"SCL-SLAM: A Scan Context-enabled LiDAR SLAM Using Factor Graph-Based Optimization","authors":"Zhiqiang Chen, Yuhua Qi, Shipeng Zhong, Dapeng Feng, Qiming Chen, Hongbo Chen","doi":"10.1109/ICUS55513.2022.9987005","DOIUrl":"https://doi.org/10.1109/ICUS55513.2022.9987005","url":null,"abstract":"In this paper, we present a complete LiDAR SLAM framework, SCL-SLAM, by integrating the loop closure module with the Scan Context descriptor into the tightly-coupled LiDAR-Inertial odometry FAST-LIO2. As a front-end, the direct LiDAR-Inertial odometry module efficiently and robustly produces motion estimates and undistorted scans. Toward the global localization based on 3D LiDAR scans, the lightweight Scan Context descriptor is used in the loop detection module. Additionally, the scan input is filtered through the keyframe selection module to improve the computation efficiency. As a back-end, a pose graph optimization is performed for the optimized trajectory and globally consistent map. SCL-SLAM is extensively evaluated on public datasets and a robot platform over various scales and environments. Experimental result shows that SCL-SLAM achieves higher accuracy than other state-of-art LiDAR SLAM systems and real-time performance. We also extend the proposed system to a centralized architecture SLAM framework for the robot team to use with 3D LiDAR observations.","PeriodicalId":345773,"journal":{"name":"2022 IEEE International Conference on Unmanned Systems (ICUS)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128086630","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Convolutional neural network has excellent representation learning ability, which makes it unique in the field of behavior prediction. This paper presents a prediction method of pedestrian behavior around intelligent vehicles, which makes use of the advantages of convolutional neural network, combines pedestrian intention and road environment information to predict pedestrian behavior around intelligent vehicle, and optimizes pedestrian avoidance module in automatic driving system. The experimental results show that the area of the closed graph composed of the predicted trajectory and the actual trajectory is 0.1269 $m$2. The method proposed in this paper can effectively predict the pedestrian behavior trajectory, ensure the safety of drivers and pedestrians to the maximum extent, and provide a new solution for intelligent vehicles and intelligent driving path planning.
{"title":"Pedestrian Behavior Prediction Method for Intelligent Vehicles Based on Convolutional Neural Network","authors":"Hongbo Gao, Xi He, Liuchang Wang, Fei Zhang, Kaiquan Cai, Xiaozhao Fang","doi":"10.1109/ICUS55513.2022.9987009","DOIUrl":"https://doi.org/10.1109/ICUS55513.2022.9987009","url":null,"abstract":"Convolutional neural network has excellent representation learning ability, which makes it unique in the field of behavior prediction. This paper presents a prediction method of pedestrian behavior around intelligent vehicles, which makes use of the advantages of convolutional neural network, combines pedestrian intention and road environment information to predict pedestrian behavior around intelligent vehicle, and optimizes pedestrian avoidance module in automatic driving system. The experimental results show that the area of the closed graph composed of the predicted trajectory and the actual trajectory is 0.1269 $m$2. The method proposed in this paper can effectively predict the pedestrian behavior trajectory, ensure the safety of drivers and pedestrians to the maximum extent, and provide a new solution for intelligent vehicles and intelligent driving path planning.","PeriodicalId":345773,"journal":{"name":"2022 IEEE International Conference on Unmanned Systems (ICUS)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131867998","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
As an important support for modern air combat intelligent auxiliary decision-making, real-time and high-precision target intent recognition addresses the foundation for realizing deep situational awareness and creating tactical opportunities. Aiming at the limitation of the existing algorithms such as dependence on empirical knowledge, difficulty in extracting the full temporal characteristics, and inability to meet the requirements of actual air combat, this paper proposes a target tactical intention recognition algorithm based on bi-directional Long Short-Term Memory (BiLSTM). Firstly, we analyze the air combat mechanism to construct the target tactical intention space based on the tactical layer. Specifically, suitable characteristics are selected to describe the intention space. We then design a recognition method considering the characteristic of the tactical intention space. Finally, compared with other algorithms, the simulation results show the effectiveness of the proposed method, which outperforms other methods in terms of accuracy at 92%. And the results are more practical.
{"title":"Tactical Intention Recognition Method of Air Combat Target Based on BiLSTM network","authors":"Xingyu Wang, Zhen Yang, Guang Zhan, Jichuan Huang, Shiyuan Chai, Deyun Zhou","doi":"10.1109/ICUS55513.2022.9986667","DOIUrl":"https://doi.org/10.1109/ICUS55513.2022.9986667","url":null,"abstract":"As an important support for modern air combat intelligent auxiliary decision-making, real-time and high-precision target intent recognition addresses the foundation for realizing deep situational awareness and creating tactical opportunities. Aiming at the limitation of the existing algorithms such as dependence on empirical knowledge, difficulty in extracting the full temporal characteristics, and inability to meet the requirements of actual air combat, this paper proposes a target tactical intention recognition algorithm based on bi-directional Long Short-Term Memory (BiLSTM). Firstly, we analyze the air combat mechanism to construct the target tactical intention space based on the tactical layer. Specifically, suitable characteristics are selected to describe the intention space. We then design a recognition method considering the characteristic of the tactical intention space. Finally, compared with other algorithms, the simulation results show the effectiveness of the proposed method, which outperforms other methods in terms of accuracy at 92%. And the results are more practical.","PeriodicalId":345773,"journal":{"name":"2022 IEEE International Conference on Unmanned Systems (ICUS)","volume":"106 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132196703","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Unmanned aerial vehicles (UAVs) path planning refers to exploring the optimal flight trajectory from the starting point to the destination that satisfies the UAV under specific constraints such as maneuverability and environmental information constraints, which is a crucial technology for the UAV mission planning. In order to enhance the efficiency and safety of the UAV path planning task, a new autonomous UAV path planning system based on deep reinforcement learning is proposed in this article. At the beginning, a new action guidance strategy based on the Deep Q-Network (DQN) algorithm is introduced via deploying the Gravity-aware Deep Q-Network (Ga-DQN) method. This strategy can effectively assist the UAVs to avoid the obstacles in the specific state. For balancing the efficiency and safety of the task, a reward scheme that introduces a safety counting mechanism is proposed to provide global guidance for the agent in Deep Reinforcement Learning (DRL). The simulation results under different obstacle densities show that the proposed novel strategy can obviously behave robust and greater efficiency compared to the traditional methods.
{"title":"Ga-DQN: A Gravity-aware DQN Based UAV Path Planning Algorithm","authors":"Zhicheng Xu, Qi Wang, Fuchen Kong, Hualong Yu, Shang Gao, Demin Pan","doi":"10.1109/ICUS55513.2022.9986557","DOIUrl":"https://doi.org/10.1109/ICUS55513.2022.9986557","url":null,"abstract":"Unmanned aerial vehicles (UAVs) path planning refers to exploring the optimal flight trajectory from the starting point to the destination that satisfies the UAV under specific constraints such as maneuverability and environmental information constraints, which is a crucial technology for the UAV mission planning. In order to enhance the efficiency and safety of the UAV path planning task, a new autonomous UAV path planning system based on deep reinforcement learning is proposed in this article. At the beginning, a new action guidance strategy based on the Deep Q-Network (DQN) algorithm is introduced via deploying the Gravity-aware Deep Q-Network (Ga-DQN) method. This strategy can effectively assist the UAVs to avoid the obstacles in the specific state. For balancing the efficiency and safety of the task, a reward scheme that introduces a safety counting mechanism is proposed to provide global guidance for the agent in Deep Reinforcement Learning (DRL). The simulation results under different obstacle densities show that the proposed novel strategy can obviously behave robust and greater efficiency compared to the traditional methods.","PeriodicalId":345773,"journal":{"name":"2022 IEEE International Conference on Unmanned Systems (ICUS)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133387867","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
With the widespread use of Internet of things(IoT), a large amount of data will be generated in the edge of network, which can facilitate a significant transformation in edge intelligent services by integrating with edge computing, 5G and artificial intelligence. However, since the intelligent edge services seriously rely on big data and computing resource, it challenges the traditional centralized data processing model. Data sharing is a promising way to tackle this problem, but some critical technical challenges still remain, such as fragile data privacy protection, inefficient data exchange and low quality of data fusion. To address these problems, a privacy-enhanced and intelligence-preserved data sharing system, name VFLChain, is proposed in this article. The proposed VFLChain is designed based on consortium blockchain and vertical federated learning, which can ensure trustworthy and secure data sharing without relying on any center platforms or third parties. Furthermore, a blockchain-assisted decentralized vertical federated learning is presented to adapt to the decentralized system and support privacy-preserved, intelligent and efficient edge data sharing, while improving quality of data through learning with different characteristic data samples. Then, a data sharing processing workflow in VFLChain is also described to demonstrated details of data sharing. The simulation experiments confirm that the proposed system and mechanism have good accuracy and stability, and guarantee an effective data sharing.
{"title":"VFLChain: Blockchain-enabled Vertical Federated Learning for Edge Network Data Sharing","authors":"Zi-Yao Cheng, Yong Pan, Yi Liu, Bowen Wang, X. Deng, Cheng Zhu","doi":"10.1109/ICUS55513.2022.9987097","DOIUrl":"https://doi.org/10.1109/ICUS55513.2022.9987097","url":null,"abstract":"With the widespread use of Internet of things(IoT), a large amount of data will be generated in the edge of network, which can facilitate a significant transformation in edge intelligent services by integrating with edge computing, 5G and artificial intelligence. However, since the intelligent edge services seriously rely on big data and computing resource, it challenges the traditional centralized data processing model. Data sharing is a promising way to tackle this problem, but some critical technical challenges still remain, such as fragile data privacy protection, inefficient data exchange and low quality of data fusion. To address these problems, a privacy-enhanced and intelligence-preserved data sharing system, name VFLChain, is proposed in this article. The proposed VFLChain is designed based on consortium blockchain and vertical federated learning, which can ensure trustworthy and secure data sharing without relying on any center platforms or third parties. Furthermore, a blockchain-assisted decentralized vertical federated learning is presented to adapt to the decentralized system and support privacy-preserved, intelligent and efficient edge data sharing, while improving quality of data through learning with different characteristic data samples. Then, a data sharing processing workflow in VFLChain is also described to demonstrated details of data sharing. The simulation experiments confirm that the proposed system and mechanism have good accuracy and stability, and guarantee an effective data sharing.","PeriodicalId":345773,"journal":{"name":"2022 IEEE International Conference on Unmanned Systems (ICUS)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131810053","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ocean front, internal wave and ocean vortex are general marine physical phenomena. The traditional observation method is from the angle of image gray level, even the existing deep learning network is based on image gray level through line detection. In this paper, a new observation method is proposed, that is, the retrieved Doppler anomaly of ocean wave motion relative to satellite antenna. For estimating Doppler anomaly, a new algorithm is proposed, which is based on Bayesian estimation method and reaches Cramer boundary through iteration. To verify the effectiveness of the algorithm, this paper uses GaoFen-3 SLC (single look complex image) SAR image. The results of local radial velocity distribution of inversion results are analyzed. The gradient distribution of local radial velocity, that is, the place where the velocity of ocean front changes the most, has the largest change in velocity gradient, which can better explain the wave modulation effect in ocean physics. Compared with the conventional method, our method can better understand and explain the marine physical phenomena by retrieving the radial velocity of ocean current.
{"title":"Observing Ocean Front by Retrieving Doppler Anomaly from GaoFen-3 SAR Images","authors":"J. Wang, Yanlang Xu, Xiaoqing Wang, Boting Pan, Mingkai Tao, Haifeng Huang","doi":"10.1109/ICUS55513.2022.9987205","DOIUrl":"https://doi.org/10.1109/ICUS55513.2022.9987205","url":null,"abstract":"Ocean front, internal wave and ocean vortex are general marine physical phenomena. The traditional observation method is from the angle of image gray level, even the existing deep learning network is based on image gray level through line detection. In this paper, a new observation method is proposed, that is, the retrieved Doppler anomaly of ocean wave motion relative to satellite antenna. For estimating Doppler anomaly, a new algorithm is proposed, which is based on Bayesian estimation method and reaches Cramer boundary through iteration. To verify the effectiveness of the algorithm, this paper uses GaoFen-3 SLC (single look complex image) SAR image. The results of local radial velocity distribution of inversion results are analyzed. The gradient distribution of local radial velocity, that is, the place where the velocity of ocean front changes the most, has the largest change in velocity gradient, which can better explain the wave modulation effect in ocean physics. Compared with the conventional method, our method can better understand and explain the marine physical phenomena by retrieving the radial velocity of ocean current.","PeriodicalId":345773,"journal":{"name":"2022 IEEE International Conference on Unmanned Systems (ICUS)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131849424","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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