静轨光学卫星与自动识别系统的目标点迹关联与误差校正

Target Point Tracks Association and Error Correction with Optical Satellite in Geostationary Orbit and Automatic Identification System

静轨光学卫星对舰船目标监视时,由于探测距离较远存在较大的目标定位误差,影响后续目标跟踪的准确性。由于任务区域主要是海面,可能无法找到地面控制点(GCP)进行坐标校正。为了提高无控下静轨光学卫星对舰船目标的定位精度,同时实现多源数据的融合,该文提出一种基于船舶自动识别系统(AIS)数据的静轨光学卫星舰船目标点迹关联与误差校正方法。利用有理多项式系数(RPC)模型实现物方坐标到像方坐标的转换,通过迭代最近点(ICP)与全局最近邻(GNN)算法进行点迹关联,由关联点对实现误差校正。利用高分4号卫星图像与AIS数据进行了实验,实验结果表明该算法具有很高的关联正确率,同时极大提高了定位精度,基本可以满足实用性要求。

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.