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| Target Point Tracks Association and Error Correction with Optical Satellite in Geostationary Orbit and Automatic Identification System |
| LIU Yong① YAO Libo② WU Yuzhou② XIU Jianjuan② ZHOU Zhimin① |
①(School of Electronic Science, National University of Defense Technology, Changsha 410073, China)
②(Institute of Information Fusion, Naval Aeronautical University, Yantai 264001, China) |
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Abstract When ship target is monitored by the geostationary optical satellite, the positioning error is large due to the long distance between the target and the satellite, which affects the accuracy of the follow-up target tracking. As the monitoring area is mainly the ocean, it may not be possible to find the Ground Control Point (GCP) for coordinate correction. In order to improve the positioning accuracy of the geostationary optical satellite for ship without GCP, and to realize the fusion of multi-source data, a novel target point association and error correction with optical satellite in geostationary orbit and ship Automatic Identification System (AIS) is proposed. By means of the Rational Polynomial Coefficient (RPC) model, AIS coordinates are transformed into image coordinates. The Iterative Closest Point (ICP) and Global Nearest Neighbor (GNN) algorithm are combined and used for data association. Then, the error is corrected using the point pair of association. Experimental results using GF-4 images and AIS data verify the feasibility of the proposed method and show that the association algorithm has a high correlation rate, and the average positioning accuracy after error correction is improved greatly compared with the positioning accuracy before correction.
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Received: 22 September 2017
Published: 02 April 2018
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| Fund:The National Natural Science Foundation of China (91538201) |
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Corresponding Authors:
LIU Yong
E-mail: xhliuyong@sina.com
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| [1] |
LIU Y, ZHOU Z, YAO L, et al. Track filtering for space-based maritime surveillance in geographic coordinates[C]. International Conference on Information Fusion, Xi’an, China, 2017: 1-6. doi: 10.23919/ICIF.2017.8009773.
|
| [2] |
李晓博, 孙文方, 李立. 静止轨道遥感卫星海面运动舰船快速检测方法[J]. 电子与信息学报, 2015, 37(8): 1862-1867. doi: 10.11999/JEIT141615.
|
|
LI Xiaobo, SUN Wenfang, and LI Li. Ocean moving ship detection method for remote sensing satellite in geostationary orbit[J]. Journal of Electronics & Information Technology, 2015, 37(8): 1862-1867. doi: 10.11999/JEIT141615.
|
| [3] |
ZHANG Z, SHAO Y, TIAN W, et al. Application potential of gf-4 images for dynamic ship monitoring[J]. IEEE Geoscience and Remote Sensing Letters, 2017, 14(6): 911-915. doi: 10.1109/LGRS.2017.2687700.
|
| [4] |
FRASER C S and HANLEY H B. Bias-compensated RPCs for sensor orientation of high-resolution satellite imagery[J]. Photogrammetric Engineering & Remote Sensing, 2005, 71(8): 909-915. doi: 10.14358/PERS.71.8.909.
|
| [5] |
PEHANI P, OTAR K, MARSETI A, et al. Automatic geometric processing for very high resolution optical satellite data based on vector roads and orthophotos[J]. Remote Sensing, 2016, 8(4): 343. doi: 10.3390/rs8040343.
|
| [6] |
齐林, 崔亚奇, 熊伟, 等. 基于距离检测的自动识别系统和对海雷达航迹抗差关联算法[J]. 电子与信息学报, 2015, 37(8): 1855-1861. doi: 10.11999/JEIT141472.
|
|
QI Lin, CUI Yaqi, XIONG Wei, et al. Anti-bias association algorithm for automatic identification system and radar based on bias detection[J]. Journal of Electronics & Information Technology, 2015, 37(8): 1855-1861. doi: 10.11999/JEIT141472.
|
| [7] |
张晖, 刘永信, 张杰, 等. 地波雷达与自动识别系统目标点迹最优关联算法[J]. 电子与信息学报, 2015, 37(3): 619-624. doi: 10.11999/JEIT140678.
|
|
ZHANG Hui, LIU Yongxin, ZHANG Jie, et al. Target point tracks optimal association algorithm with surface wave radar and automatic identification system[J]. Journal of Electronics & Information Technology, 2015, 37(3): 619-624. doi: 10.11999/JEIT140678.
|
| [8] |
JI Y, ZHANG J, MENG J, et al. Point association analysis of vessel target detection with SAR, HFSWR and AIS[J]. Acta Oceanologica Sinica, 2014, 33(9): 73-81. doi: 10.1007/s13131.
|
| [9] |
KAZIMIERSKI W. Proposal of neural approach to maritime radar and automatic identification system tracks association [J]. IET Radar, Sonar & Navigation, 2016, 11(5): 729-735. doi: 10.1049/iet-rsn.2016.0409.
|
| [10] |
CHATURVEDI S K, YANG C S, OUCHI K, et al. Ship recognition by integration of SAR and AIS[J]. The Journal of Navigation, 2012, 65(2): 323-337. doi: 10.1017/S03734633 11000749.
|
| [11] |
ZHAO Z, JI K, XING X, et al. Ship surveillance by integration of space-borne SAR and AIS-further research[J]. The Journal of Navigation, 2014, 67(2): 295-309. doi: 10.1017 /S0373463313000702.
|
| [12] |
ZHANG H, LIU Y, JI Y, et al. Multi-feature maximum likelihood association with space-borne SAR, HFSWR and AIS[J]. The Journal of Navigation, 2017, 70(2): 359-378. doi: 10.1017/S037346331600062X.
|
| [13] |
BESL P J and MCKAY H D. A method for registration of 3-D shapes[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2002, 14(2): 239-256. doi: 10.1109/ 34.121791.
|
| [14] |
BERGSTRÖM P and EDLUND O. Robust registration of point sets using iteratively reweighted least squares[J]. Computational Optimization & Applications, 2014, 58(3): 543-561. doi: 10.1007/s10589-014-9643-2.
|
| [15] |
FISCHLER M A and BOLLES R C. Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography[J]. Communications of the ACM, 1981, 24(6): 381-395. doi: 10.1145/358669.358692.
|
| [16] |
EL-DARYMLI K, MCGUIRE P, POWER D, et al. Target detection in synthetic aperture radar imagery: A state-of- the-art survey[J]. Journal of Applied Remote Sensing, 2013, 7(1): 071598. doi: 10.1117/1.JRS.7.071598.
|
|
|
|
