摘要
传统的高分辨距离像检测法本质上是将检测窗内大于固定门限值的距离单元积累,从而确定目标的有无,这些方法忽略了目标散射点在空间中连续分布的特点,易将检测窗内明显远离目标的强噪声点积累,造成检测率下降。针对这个问题,提出一种基于幅空联合分布的高分辨距离像检测方法,该方法联合目标散射点幅值空间分布两个维度的信息,使用迭代聚类对检测窗内高分辨距离像分割,并结合虚警率计算检测门限。通过积累目标能量来确定检验统计量,将其与检测门限对比判断目标有无。使用了4种典型的散射点分布模型和一个实测目标的距离像进行Monte Carlo试验,验证了提出的检测器比传统检测器有更好的检测性能。
Conventional high-resolution range profile detection methods essentially accumulate distance units within the detection window that are larger than a fixed threshold value to determine the presence or absence of a target. These methods ignore the characteristics of the continuous distribution of target scattering points in space and tend to accumulate strong noise points within the detection window that are obviously far from the target, resulting in a decrease in detection rate. To address this problem, this paper proposes a high-resolution range profile detection method based on the joint amplitude-space distribution, which combines the information of two dimensions of the spatial distribution of the target scattering point amplitude, uses iterative clustering to segment the high-resolution range profile within the detection window, and combines the false alarm rate to calculate the detection threshold. This method determines the test statistic by accumulating the target energy, and compares it with the detection threshold to determine the presence or absence of the target. In this paper, four typical scattering point distribution models and a range profile of a real target are used for Monte Carlo tests to verify that the proposed detector has better detection performance than conventional detectors.
作者
汤留阳
齐向阳
范怀涛
Tang Liuyang;Qi Xiangyang;Fan Huaitao(Aerospace Information Research Institute,Chinese Academy of Sciences,Beijing 100190,China;School of Electronic,Electrical and Communication Engineering,University of Chinese Academy of Sciences,Beijing 100049,China)
出处
《国外电子测量技术》
北大核心
2023年第2期157-163,共7页
Foreign Electronic Measurement Technology
关键词
高分辨距离像
目标检测
幅空联合分布
聚类
high-resolution range profile
target detection
magnitude and space joint distribution
clustering
