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
Directional wave spectra and integrated wave parameters can be derived from X-band radar sea surface images.A vessel on the sea surface has a significant influence on wave parameter inversions that can be seen as inte...Directional wave spectra and integrated wave parameters can be derived from X-band radar sea surface images.A vessel on the sea surface has a significant influence on wave parameter inversions that can be seen as intensive backscatter speckles in X-band wave monitoring radar sea surface images.A novel algorithm to eliminate the interference of vessels in ocean wave height inversions from X-band wave monitoring radar is proposed.This algorithm is based on the characteristics of the interference.The principal components(PCs) of a sea surface image sequence are extracted using empirical orthogonal function(EOF)analysis.The standard deviation of the PCs is then used to identify vessel interference within the image sequence.To mitigate the interference,a suppression method based on a frequency domain geometric model is applied.The algorithm framework has been applied to OSMAR-X,a wave monitoring system developed by Wuhan University,based on nautical X-band radar.Several sea surface images captured on vessels by OSMAR-X are processed using the method proposed in this paper.Inversion schemes are validated by comparisons with data from in situ wave buoys.The root-mean-square error between the significant wave heights(SWH) retrieved from original interference radar images and those measured by the buoy is reduced by 0.25 m.The determinations of surface gravity wave parameters,in particular SWH,confirm the applicability of the proposed method.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Supported by the National High Technology Research and Development Program of China(863 Program)(Nos.2012AA091701,2012AA091702)the National Natural Science Foundation of China(No.61401316)+1 种基金the PhD.Programs Foundation of Ministry of Education of China(No.20130141110053)the Fundamental Research Fund for the Central Universities of China(No.2014212020203)
文摘Directional wave spectra and integrated wave parameters can be derived from X-band radar sea surface images.A vessel on the sea surface has a significant influence on wave parameter inversions that can be seen as intensive backscatter speckles in X-band wave monitoring radar sea surface images.A novel algorithm to eliminate the interference of vessels in ocean wave height inversions from X-band wave monitoring radar is proposed.This algorithm is based on the characteristics of the interference.The principal components(PCs) of a sea surface image sequence are extracted using empirical orthogonal function(EOF)analysis.The standard deviation of the PCs is then used to identify vessel interference within the image sequence.To mitigate the interference,a suppression method based on a frequency domain geometric model is applied.The algorithm framework has been applied to OSMAR-X,a wave monitoring system developed by Wuhan University,based on nautical X-band radar.Several sea surface images captured on vessels by OSMAR-X are processed using the method proposed in this paper.Inversion schemes are validated by comparisons with data from in situ wave buoys.The root-mean-square error between the significant wave heights(SWH) retrieved from original interference radar images and those measured by the buoy is reduced by 0.25 m.The determinations of surface gravity wave parameters,in particular SWH,confirm the applicability of the proposed method.
