引用本文:彭书娟,曲长文,李健伟.K近邻优化估计的SAR图像建模与目标检测算法[J].控制与决策,2020,35(9):2199-2206
【打印本页】   【HTML】   【下载PDF全文】   查看/发表评论  【EndNote】   【RefMan】   【BibTex】
←前一篇|后一篇→ 过刊浏览    高级检索
本文已被:浏览次   下载 本文二维码信息
码上扫一扫!
分享到: 微信 更多
K近邻优化估计的SAR图像建模与目标检测算法
彭书娟1, 曲长文2, 李健伟1
(1. 海军航空大学研究生三队,山东烟台264001;2. 海军航空大学,山东烟台264001)
摘要:
在非均匀杂波环境下的合成孔径雷达(synthetic aperture radar,SAR)图像背景建模问题中,针对非参量建模算法Parzen窗估计严重依赖于窗宽设置及最优核函数选择的问题,提出一种基于K近邻优化的概率密度函数估计算法,解决因固定近邻数而导致估计不准确甚至不能估计的问题.该算法不需要图像的任何先验知识,且无需考虑窗宽的设置及最优核函数的选择问题.与Parzen窗估计、K分布和$G^0$分布的对比实验表明,所提出的K近邻优化估计算法可以实现对单峰、多峰甚至不规则图像数据的准确建模,优于K分布和$G^0$分布;同时,对图像首尾数据的处理优于Parzen窗估计.实验结果验证了所提出方法对SAR图像杂波建模的精确性、鲁棒性和简便性,以及全局恒虚警率目标检测的有效性.
关键词:  SAR图像统计建模  K近邻优化估计  平均区域体积  核密度估计  恒虚警率  目标检测
DOI:10.13195/j.kzyjc.2019.0051
分类号:TN957.52
基金项目:国家自然科学基金项目(615714541).
K nearest neighbors optimized estimation algorithm for SAR image statistical modeling and target detection
PENG Shu-juan1,QU Chang-wen2,LI Jian-wei1
(1.The 3rd Graduate Student Team,Naval Aviation University,Yantai264001,China;2.Naval Aviation University,Yantai264001,China)
Abstract:
Clutter statistical modeling for synthetic aperture radar(SAR) measurements in nonhomogeneous clutter environments is a complex and challenging task. In order to overcome the dependence of the Parzen window estimation in the non-parametric modeling algorithm for setting the window width and picking the optimal kernel function, a new K nearest neighbors optimized estimation algorithm is proposed. The proposed algorithm solves the problem of that the estimation is inaccurate or cannot be estimated due to the fixed neighbor number. Meanwhile, the algorithm neither requires any prior knowledge nor needs to set the window width and select the optimal kernel function. Compared with Parzen window estimation, K distribution and $G^0$ distribution, the proposed K-nearest neighbor optimization estimation algorithm can accurately model single-peak, multi-peak and even irregular image data, which is better than K distribution and $G^0$ distribution. And the processing of the first and last data of the image is better than the Parzen window estimation. The experimental results verify the accuracy, robustness and simplicity of the proposed method for background clutter modeling of SAR images, and the effectiveness of global constant false alarm rate target detection.
Key words:  SAR image  K nearest neighbors optimized estimation  average regional volume  kernel density estimation  CFAR  target detection

用微信扫一扫

用微信扫一扫