基于马氏随机场模型的空间近邻目标检测及量测划分

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....

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Bibliographic Details
Published in红外与毫米波学报 Vol. 34; no. 5; pp. 599 - 605
Main Author 王雪莹 李骏 盛卫东 安玮
Format Journal Article
LanguageChinese
Published 国防科技大学电子科学与工程学院,湖南长沙,410073 2015
Subjects
多目标检测
天基光学系统
空间近邻目标
量测划分
马氏随机场
closely spaced objects
多目标检测
空间近邻目标
multiple targets detection
马氏随机场
pixel partition
Markov random field
量测划分
天基光学系统
space-based optical system
Online AccessGet full text
ISSN1001-9014
DOI10.11972/j.issn.1001-9014.2015.05.015

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Summary: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.
Bibliography:31-1577/TN
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.
Markov random field; space-based optical system; closely spaced objects; multiple targets detection; pixel partition
WANG Xue-Ying, LI Jun, SHENG Wei-Dong, AN Wei （College of Electronic Science and Engineering, National University of Defense Technology, Changsha 410073, China）
ISSN:1001-9014
DOI:10.11972/j.issn.1001-9014.2015.05.015