天基光学传感器对空间近邻目标的像平面跟踪过程中,传统方法在单帧恒虚警检测后进行量测划分,采用的虚警率过高可能引入较多的杂波点,过低则群目标在像平面的部分信息损失.在分析空间近邻目标在像平面特征的基础上,提出一种使用马氏随机场模型进行预检测处理然后以k-均值进行量测划分的方法,仿真结果表明,相比传统方法,基于马氏随机场模型的空间近邻目标检测及量测划分准确率更高,且在信噪比较低的情况下,性能改善明显.

In space-based optical systems, during the pixel-plane tracking for closely spaced objects (CSOs), in traditional methods, pixels are partitioned after constant false alarm rate detection (CFAR), where higher false alarm rate results in more clutter measurements while lower false alarm rate results in the loss of targets’ information. To solve this problem, CSOs’ feature on pixel-plane were analyzed and a pre-detecting method using Markov random field model(MRF) was proposed. Then pixels were partitioned with k-means. Simulations indicated that detection and partition with MRF provides higher performance than traditional method, especially when signal-noise ratio is poor.