CAJ | 학술논문

本文主要研究基于图像的强空域杂波背景下微小目标检测.本文提出了一种新的基于邻域梯度差平方累积函数最小原则的背景杂波自适应预测算法.该算法能显著改善微小目标的信杂比(SCNR).试验证明,本算法相对于已有的多种算法,有着更好的性能.本文还引入了一种基于统计分析的快速检测算法,该算法能在较低的虚警概率情况下,获得更高的检测概率.理论分析及仿真表明,本文提出的检测系统在微小目标检测中,具有很高的实用性.
본문주요연구기우도상적강공역잡파배경하미소목표검측.본문제출료일충신적기우린역제도차평방루적함수최소원칙적배경잡파자괄응예측산법.해산법능현저개선미소목표적신잡비(SCNR).시험증명,본산법상대우이유적다충산법,유착경호적성능.본문환인입료일충기우통계분석적쾌속검측산법,해산법능재교저적허경개솔정황하,획득경고적검측개솔.이론분석급방진표명,본문제출적검측계통재미소목표검측중,구유흔고적실용성.
This paper addresses the problem of detecting small targets with very small spatial extent that are masked by spatially strong background clutter in image data. A novel adaptive prediction algorithm of background clutter is proposed based on decreasing the Cumulation-squared Error (CSE) of gradient in neighborhood. The algorithm can improve the Signal Clutter Noise Ratio (SCNR) of small targets evidently. Many experiments prove that the algorithm has better performance than traditional methods. And a fast detecting algorithm based on statistical analysis is introduced. It can get higher probability of detection based on lower probability of false alarm than other methods. The theoretic analysis and many simulations verify the validity of the method.