空间非合作目标跟踪技术可以在多方面发挥重要作用, 目前效果较好的图像跟踪算法多是基于视频流处理, 但是由于面对的工况与航天应用面对工况不同, 在跟踪精度、运算速度、预警率和虚警率等要求上不满足空间目标跟踪需求与任务要求, 并且运算复杂难以在航天器中实现, 不适合天基卫星跟踪。为解决这一问题, 一种面向空间应用的卫星目标高精度跟踪算法被提出, 该算法以图像相关、曲线拟合、卡尔曼滤波、SURF算法为基础, 并将预测、跟踪和矫正过程相融合, 最终获得在天基平台中具有可行性的高速稳定跟踪算法。相关实验表明, 这种算法可以对平面内自由旋转、0.4~2.1倍尺度内缩放、有光照变化的图像进行连续跟踪, 仿真试验平均跟踪误差小于0.9像素且大多数工况下计算速度高于200帧/s, 并且算法对图像模糊、高斯噪声以及椒盐噪声都有较好兼容能力,对于实际模型目标跟踪仍有稳定跟踪能力。

The space-based non-cooperative target tracking technology could play a crucial part in many aspects of the space application. Currently most well-behaved algorithms were based on video stream. Owing to different working conditions, their tracking precision, speed of operation, warning rate and false alarm rate were dissatisfied with the requirement of space-based satellite tracking systems and missions. Furthermore, the video stream tracking algorithms were too complicated for space-based conditions, where the processors were weaker than those on the ground. To solve these problem, an algorithm based on image correlation, curve fitting, Kalman filter, and SURF algorithm and combined with prediction, tracking and rectification systems was proposed. The algorithm could achieve high speed and high accuracy and be satisfied with the space-based computing environment. Proved by the image simulation experiment and semi-physics simulation, this algorithm could continuously track the target rotated in in-plane arbitrary angle, scaled from 0.4 to 2.1, and handle illumination change. The mean error of image tracking simulation experiment result was lower than 0.9 pixel and frame rate was more than 200 frames per second under most conditions. This algorithm could also deal with image blur, Gaussian noise and salt and pepper noise. Satellite model tracking experiment results showed that the algorithm also had a stably tracking performance for practical satellite model.