海面小目标检测的自适应背景感知研究  

Adaptive background sensing for small target detection in sea clutter

作　　者：楼奇哲 寇鹏飞 姚元[1] Lou Qizhe;Kou Pengfei;Yao Yuan(Nanjing Research Institute of Electronics,Nanjing 210039,China)

机构地区：[1]南京电子技术研究所

出　　处：《电子测量技术》2018年第18期22-26,共5页Electronic Measurement Technology

摘　　要：海面小目标检测是舰载雷达的重要使命,为了优化海杂波下的检测环境,并提高小目标检测的信杂比,引入背景估计思想,建立了基于径向基神经网络的海杂波背景感知模型。通过研究雷达实际测量数据,提出了简单有效的数据预处理方法,通过构造适合处理一维回波数据的神经网络模型,并采用正交最小二乘学习算法对模型结构进行自适应调整,减小了模型复杂度并提升了模型的性能,从而实现海杂波背景的良好感知。最后,基于实测数据对模型进行性能验证,针对杂波对消前后的数据计算信杂比,得到信杂比改善因子均值达到了2 dB。结果显示本方法优化了海杂波下的检测环境,并能够在一定程度上改善小目标检测的信杂比,表明了本方法的有效性。Small target detection in sea clutter is an important mission of shipborne radar. In order to optimize the detection environment under sea clutter and improve signal-to-clutter ratio for small target, this paper introduces the background estimation idea, and establishes a background perception model based on radial basis function neural network. Through analyzing the actual data of radar, a simple and effective data preprocessing method is proposed. By constructing a neural network model suitable for processing one-dimensional echo data, and using the orthogonal least squares algorithm to adaptively adjust the model, the complexity of model is reduced and the performance is improved, so as to achieve a good perception of the background. Finally, based on actual data, the performance is verified. Signal-to-clutter ratio is calculated before and after clutter cancellation, and average value of improvement factor reaches 2 dB. Results show that this method optimizes detection environment under sea clutter, and can improve signal-to-clutter ratio for small target to a certain extent, indicating the effectiveness of the method.

关 键 词：小目标检测 海杂波 背景估计 神经网络 

分 类 号：TP389.1[自动化与计算机技术—计算机系统结构]