High-frequency surface wave radar（HFSWR） and automatic identification system（AIS） are the two most important sensors used for vessel tracking.The HFSWR can be applied to tracking all vessels in a detection area,wh...High-frequency surface wave radar（HFSWR） and automatic identification system（AIS） are the two most important sensors used for vessel tracking.The HFSWR can be applied to tracking all vessels in a detection area,while the AIS is usually used to verify the information of cooperative vessels.Because of interference from sea clutter,employing single-frequency HFSWR for vessel tracking may obscure vessels located in the blind zones of Bragg peaks.Analyzing changes in the detection frequencies constitutes an effective method for addressing this deficiency.A solution consisting of vessel fusion tracking is proposed using dual-frequency HFSWR data calibrated by the AIS.Since different systematic biases exist between HFSWR frequency measurements and AIS measurements,AIS information is used to estimate and correct the HFSWR systematic biases at each frequency.First,AIS point measurements for cooperative vessels are associated with the HFSWR measurements using a JVC assignment algorithm.From the association results of the cooperative vessels,the systematic biases in the dualfrequency HFSWR data are estimated and corrected.Then,based on the corrected dual-frequency HFSWR data,the vessels are tracked using a dual-frequency fusion joint probabilistic data association（JPDA）-unscented Kalman filter（UKF） algorithm.Experimental results using real-life detection data show that the proposed method is efficient at tracking vessels in real time and can improve the tracking capability and accuracy compared with tracking processes involving single-frequency data.展开更多
The trajectory planning and tracking control for an underactuated unmanned surface vessel(USV) were addressed.The reference trajectory was generated by a virtual USV,and the error equation of trajectory tracking for u...The trajectory planning and tracking control for an underactuated unmanned surface vessel(USV) were addressed.The reference trajectory was generated by a virtual USV,and the error equation of trajectory tracking for underactuated USV was obtained,which transformed the tracking and stabilization problem of underactuated USV into the stabilization problem of the trajectory tracking error equation.A nonlinear state feedback controller was proposed based on backstepping technique and Lyapunov's direct method.By means of Lyapunov analysis,it is proved that the proposed controller ensures that the solutions of closed loop system have the ultimate boundedness property.Numerical simulation results are presented to validate the effectiveness and robustness of the proposed controller.展开更多
"雪龙"号极地科考船是推动我国极地科学考察事业发展的重要工具,"雪龙"号在数十次的极地科考过程中累积了大量的航迹数据,其中蕴含的巨大价值亟须挖掘。针对科考船的航迹分段是将科考船移动轨迹分为停留与行驶两部..."雪龙"号极地科考船是推动我国极地科学考察事业发展的重要工具,"雪龙"号在数十次的极地科考过程中累积了大量的航迹数据,其中蕴含的巨大价值亟须挖掘。针对科考船的航迹分段是将科考船移动轨迹分为停留与行驶两部分,合理的分段方法可以分离出信息更丰富的航迹段,有利于航迹知识提取。然而,由于原始航迹信息密度分布不均等原因,现有的航迹分段方法往往会造成分段过多等问题,结果并不理想。本文针对该问题,提出了一种针对科考航迹整体的时域差分(Time Domain Difference,TDD)分段方法。本方法基于时间域对航速进行差分处理,有效降低了因为航速波动频繁对分段结果的影响。同时,考虑到该方法的差分步长在航迹处理过程中的不明确性,本文将差分后航迹的路程损失和航速波动幅值进行归一化处理,提出了航迹差分时间步长的动态确定方法,并以速率阈值对航迹进行分段。最后本文以第29次南极科考航迹数据为例,将本方法与经典的具有噪声的基于密度的聚类方法DBSCAN(Density-Based Spatial Clustering of Applications with Noise)进行了比较,实验结果表明本文提出的方法可有效降低航迹分段时分段过多的问题,在分段准确性和时间效率等方面结果更优。展开更多
基金The National Natural Science Foundation of China under contract No.61362002the Marine Scientific Research Special Funds for Public Welfare of China under contract No.201505002
文摘High-frequency surface wave radar（HFSWR） and automatic identification system（AIS） are the two most important sensors used for vessel tracking.The HFSWR can be applied to tracking all vessels in a detection area,while the AIS is usually used to verify the information of cooperative vessels.Because of interference from sea clutter,employing single-frequency HFSWR for vessel tracking may obscure vessels located in the blind zones of Bragg peaks.Analyzing changes in the detection frequencies constitutes an effective method for addressing this deficiency.A solution consisting of vessel fusion tracking is proposed using dual-frequency HFSWR data calibrated by the AIS.Since different systematic biases exist between HFSWR frequency measurements and AIS measurements,AIS information is used to estimate and correct the HFSWR systematic biases at each frequency.First,AIS point measurements for cooperative vessels are associated with the HFSWR measurements using a JVC assignment algorithm.From the association results of the cooperative vessels,the systematic biases in the dualfrequency HFSWR data are estimated and corrected.Then,based on the corrected dual-frequency HFSWR data,the vessels are tracked using a dual-frequency fusion joint probabilistic data association（JPDA）-unscented Kalman filter（UKF） algorithm.Experimental results using real-life detection data show that the proposed method is efficient at tracking vessels in real time and can improve the tracking capability and accuracy compared with tracking processes involving single-frequency data.
基金Project(2013M540271)supported by the Postdoctoral Science Foundation of ChinaProject(HEUCF1321003)support by the Basic Research Foundation of Central University,ChinaProject(51209050)supported by the National Natural Science Foundation of China
文摘The trajectory planning and tracking control for an underactuated unmanned surface vessel(USV) were addressed.The reference trajectory was generated by a virtual USV,and the error equation of trajectory tracking for underactuated USV was obtained,which transformed the tracking and stabilization problem of underactuated USV into the stabilization problem of the trajectory tracking error equation.A nonlinear state feedback controller was proposed based on backstepping technique and Lyapunov's direct method.By means of Lyapunov analysis,it is proved that the proposed controller ensures that the solutions of closed loop system have the ultimate boundedness property.Numerical simulation results are presented to validate the effectiveness and robustness of the proposed controller.
文摘"雪龙"号极地科考船是推动我国极地科学考察事业发展的重要工具,"雪龙"号在数十次的极地科考过程中累积了大量的航迹数据,其中蕴含的巨大价值亟须挖掘。针对科考船的航迹分段是将科考船移动轨迹分为停留与行驶两部分,合理的分段方法可以分离出信息更丰富的航迹段,有利于航迹知识提取。然而,由于原始航迹信息密度分布不均等原因,现有的航迹分段方法往往会造成分段过多等问题,结果并不理想。本文针对该问题,提出了一种针对科考航迹整体的时域差分(Time Domain Difference,TDD)分段方法。本方法基于时间域对航速进行差分处理,有效降低了因为航速波动频繁对分段结果的影响。同时,考虑到该方法的差分步长在航迹处理过程中的不明确性,本文将差分后航迹的路程损失和航速波动幅值进行归一化处理,提出了航迹差分时间步长的动态确定方法,并以速率阈值对航迹进行分段。最后本文以第29次南极科考航迹数据为例,将本方法与经典的具有噪声的基于密度的聚类方法DBSCAN(Density-Based Spatial Clustering of Applications with Noise)进行了比较,实验结果表明本文提出的方法可有效降低航迹分段时分段过多的问题,在分段准确性和时间效率等方面结果更优。
