总结和分析了地表形变D-InSAR监测的主要方法和当前所面临的主要问题。针对常规D-InSAR技术中大气相位和低相干区域相位解缠,分别介绍了基于Delaunay三角网的不规则格网解缠方法、累积干涉纹图处理方法(Stack ing Interferogram s)、永...总结和分析了地表形变D-InSAR监测的主要方法和当前所面临的主要问题。针对常规D-InSAR技术中大气相位和低相干区域相位解缠,分别介绍了基于Delaunay三角网的不规则格网解缠方法、累积干涉纹图处理方法(Stack ing Interferogram s)、永久性散射体(PS)技术以及角反射器干涉测量(CR-InSAR)方法,分析了各自的适用条件和优缺点。此外,对有限数据量条件下低相干区域大气相位校正和相干目标识别等问题进行了重点讨论。立足于工程应用需要,分别对D-InSAR测量地表形变的参数要求、测量结果的精度验证、D-InSAR测量值与形变的关系、大区域处理以及形变场时空演变等问题进行了分析和讨论。展开更多
The Time-Domain-Integral-Equation (TDIE) method is proposed to analyze transient scattering interaction between a two-dimensional infinitely long conducting target with an arbitrary cross section and a one-dimensional...The Time-Domain-Integral-Equation (TDIE) method is proposed to analyze transient scattering interaction between a two-dimensional infinitely long conducting target with an arbitrary cross section and a one-dimensional rough surface. Based on the electric-field-integral-equation in time domain, the explicit and implicit solutions of MOT (Marching-on-time) are derived and presented. The current response at the center of the rough surface and the far electric field response with time in the composite model are calculated and analyzed. The numerical results are compared and verified with those obtained by conventional MOM-IDFT (Method of Moment-inverse discrete Fourier transform). Finally, the influence of the size, the location of the target and the incident angle on the current response and the far electric fields response are discussed in detail.展开更多
This paper is concerned with the cooperative target tracking of multiple autonomous surface vehicles(ASVs)under switching interaction topologies.For the target to be tracked,only its position can be measured/received ...This paper is concerned with the cooperative target tracking of multiple autonomous surface vehicles(ASVs)under switching interaction topologies.For the target to be tracked,only its position can be measured/received by some of the ASVs,and its velocity is unavailable to all the ASVs.A distributed extended state observer taking into consideration switching topologies is designed to integrally estimate unknown target dynamics and neighboring ASVs'dynamics.Accordingly,a novel kinematic controller is designed,which takes full advantage of known information and avoids the approximation of some virtual control vectors.Moreover,a disturbance observer is presented to estimate unknown time-varying environmental disturbance.Furthermore,a distributed dynamic controller is designed to regulate the involved ASVs to cooperatively track the target.It enables each ASV to adjust its forces and moments according to the received information from its neighbors.The effectiveness of the derived results is demonstrated through cooperative target tracking performance analysis for a tracking system composed of five interacting ASVs.展开更多
To address the challenges of missed detections in water surface target detection using solely visual algorithms in unmanned surface vehicle(USV)perception,this paper proposes a method based on the fusion of visual and...To address the challenges of missed detections in water surface target detection using solely visual algorithms in unmanned surface vehicle(USV)perception,this paper proposes a method based on the fusion of visual and LiDAR point-cloud projection for water surface target detection.Firstly,the visual recognition component employs an improved YOLOv7 algorithmbased on a self-built dataset for the detection of water surface targets.This algorithm modifies the original YOLOv7 architecture to a Slim-Neck structure,addressing the problemof excessive redundant information during feature extraction in the original YOLOv7 network model.Simultaneously,this modification simplifies the computational burden of the detector,reduces inference time,and maintains accuracy.Secondly,to tackle the issue of sample imbalance in the self-built dataset,slide loss function is introduced.Finally,this paper replaces the original Complete Intersection over Union(CIoU)loss function with the Minimum Point Distance Intersection over Union(MPDIoU)loss function in the YOLOv7 algorithm,which accelerates model learning and enhances robustness.To mitigate the problem of missed recognitions caused by complex water surface conditions in purely visual algorithms,this paper further adopts the fusion of LiDAR and camera data,projecting the threedimensional point-cloud data from LiDAR onto a two-dimensional pixel plane.This significantly reduces the rate of missed detections for water surface targets.展开更多
文摘总结和分析了地表形变D-InSAR监测的主要方法和当前所面临的主要问题。针对常规D-InSAR技术中大气相位和低相干区域相位解缠,分别介绍了基于Delaunay三角网的不规则格网解缠方法、累积干涉纹图处理方法(Stack ing Interferogram s)、永久性散射体(PS)技术以及角反射器干涉测量(CR-InSAR)方法,分析了各自的适用条件和优缺点。此外,对有限数据量条件下低相干区域大气相位校正和相干目标识别等问题进行了重点讨论。立足于工程应用需要,分别对D-InSAR测量地表形变的参数要求、测量结果的精度验证、D-InSAR测量值与形变的关系、大区域处理以及形变场时空演变等问题进行了分析和讨论。
基金Supported by the National Natural Science Foundation of China (Grant No. 60571058)the Specialized Research Fund for the Doctoral Program of Higher Education of China (Grant No. 20070701010)
文摘The Time-Domain-Integral-Equation (TDIE) method is proposed to analyze transient scattering interaction between a two-dimensional infinitely long conducting target with an arbitrary cross section and a one-dimensional rough surface. Based on the electric-field-integral-equation in time domain, the explicit and implicit solutions of MOT (Marching-on-time) are derived and presented. The current response at the center of the rough surface and the far electric field response with time in the composite model are calculated and analyzed. The numerical results are compared and verified with those obtained by conventional MOM-IDFT (Method of Moment-inverse discrete Fourier transform). Finally, the influence of the size, the location of the target and the incident angle on the current response and the far electric fields response are discussed in detail.
基金supported in part by the National Science Foundation of China(61873335,61833011)the Project of Scie nce and Technology Commission of Shanghai Municipality,China(20ZR1420200,21SQBS01600,19510750300,21190780300)。
文摘This paper is concerned with the cooperative target tracking of multiple autonomous surface vehicles(ASVs)under switching interaction topologies.For the target to be tracked,only its position can be measured/received by some of the ASVs,and its velocity is unavailable to all the ASVs.A distributed extended state observer taking into consideration switching topologies is designed to integrally estimate unknown target dynamics and neighboring ASVs'dynamics.Accordingly,a novel kinematic controller is designed,which takes full advantage of known information and avoids the approximation of some virtual control vectors.Moreover,a disturbance observer is presented to estimate unknown time-varying environmental disturbance.Furthermore,a distributed dynamic controller is designed to regulate the involved ASVs to cooperatively track the target.It enables each ASV to adjust its forces and moments according to the received information from its neighbors.The effectiveness of the derived results is demonstrated through cooperative target tracking performance analysis for a tracking system composed of five interacting ASVs.
基金supported by the National Natural Science Foundation of China(No.51876114)the Shanghai Engineering Research Center of Marine Renewable Energy(Grant No.19DZ2254800).
文摘To address the challenges of missed detections in water surface target detection using solely visual algorithms in unmanned surface vehicle(USV)perception,this paper proposes a method based on the fusion of visual and LiDAR point-cloud projection for water surface target detection.Firstly,the visual recognition component employs an improved YOLOv7 algorithmbased on a self-built dataset for the detection of water surface targets.This algorithm modifies the original YOLOv7 architecture to a Slim-Neck structure,addressing the problemof excessive redundant information during feature extraction in the original YOLOv7 network model.Simultaneously,this modification simplifies the computational burden of the detector,reduces inference time,and maintains accuracy.Secondly,to tackle the issue of sample imbalance in the self-built dataset,slide loss function is introduced.Finally,this paper replaces the original Complete Intersection over Union(CIoU)loss function with the Minimum Point Distance Intersection over Union(MPDIoU)loss function in the YOLOv7 algorithm,which accelerates model learning and enhances robustness.To mitigate the problem of missed recognitions caused by complex water surface conditions in purely visual algorithms,this paper further adopts the fusion of LiDAR and camera data,projecting the threedimensional point-cloud data from LiDAR onto a two-dimensional pixel plane.This significantly reduces the rate of missed detections for water surface targets.
