基于马氏随机场模型的空间近邻目标检测及量测划分

Detection and partition for closely spaced objects using Markov random field model

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）,w here higher false alarm rate results in more clutter measurements w hile low er false alarm rate results in the loss of targets＇ information. To solve this problem,CSOs＇ feature on pixel-plane w ere analyzed and a pre-detecting method using M arkov random field model（ MRF） was proposed. Then pixels were partitioned with k-means. Simulations indicated that detection and partition w ith M RF provides higher performance than traditional method,especially w hen signal-noise ratio is poor.