摘要
高频地波雷达(High-frequency surface wave radar,HFSWR)在超视距舰船目标检测跟踪中有广泛应用.然而,HFSWR工作频段的电磁环境十分复杂,舰船目标信号往往被淹没在各种噪声中.本文提出一种基于最优误差自校正极限学习机(Optimized error self-adjustment extreme learning machine,OES-ELM)的HFSWR海面目标识别算法.该算法利用二级级联分类策略,可以显著提高目标的检测效率.首先利用灰度特征和线性分类器快速找出目标的潜在区域.然后利用Haar-like特征和OES-ELM分类器进一步辨识目标和海杂波.在OES-ELM中,首先利用L1=2正则算子裁剪隐层中的\微弱"神经元,以得到隐层神经元的最优个数;其次,通过网络误差回传至隐含层使网络的隐层权值和输出层权值迭代更新至最优状态.实验结果表明:和标准ELM相比,提出的OES-ELM网络具有更好的性能;此外,基于OES-ELM的HFSWR目标检测方法具有良好的实时性和目标检测性能.
High-frequency surface wave radar(HFSWR)has been widely applied in ship targets detection and tracking beyond the line-of-sight limitation.However,the detection background of HFSWR is completely complex,in which the ship targets usually polluted by all kinds of noises.In this paper,a novel ship target detection method based on optimized error self-adjustment extreme learning machine(OES-ELM)for RD images of HFSWR is presented.Through the application of two-stage cascade classification strategy,the proposed approach can impressively increase the detection efficiency in real time.Firstly,gray-scale feature and a linear classifier are adopted to obtain the target candidate areas.Then,Haar-like features and a optimized ELM are proposed to identify ship targets from precisely.In the proposed OES-ELM,the sparse solution of output weights is found by L1/2 regularizer process,in which the optimal hidden neurons can be obtained by pruning the"weak"nodes.In addition,both the output and hidden weights are updated to optimal value by pulling back the output error to the hidden layer.Experimental results show that the proposed OES-ELM has better generalization performance.Furthermore,the proposed method has favorable real time and target detection performance.
作者
张万栋
李庆忠
黎明
武庆明
ZHANG Wan-Dong;LI Qing-Zhong;LI Ming;QMJonathan Wu(Department of Engineering,Ocean University of China Qingdao 266100,China;Department of Electrical and Com-puter Engineering,University of Windsor,Windsor N9B 3P4,Canada)
出处
《自动化学报》
EI
CAS
CSCD
北大核心
2021年第1期108-120,共13页
Acta Automatica Sinica
基金
国家重点研发计划(2017YFC1405202)
海洋公益性行业科研专项(201605002)
国家自然科学基金(61132005)资助。
关键词
高频地波雷达
极限学习机
目标检测
RD图像
High frequency surface wave radar(HFSWR)
extreme learning machine(ELM)
target detection
RD image
