摘要
目前,海面小目标已成为海洋雷达探测的重点和难点。这类目标具有低信杂比和弱机动性,导致传统检测器性能损失严重,出现检测概率低和虚警率高的问题。为了有效探测目标,常常需要雷达具备长时观测的能力,从而提高目标回波的信杂比。新体制下全极化雷达可保证足够的观测时间,且进一步拓宽信息的维度。为此,本文提出一种基于改进1D-AlexNet的新检测方法,通过充分挖掘全极化雷达的回波信息,全面提高海面小目标探测能力。首先,从时域、频域、时频域、极化域,提取24个有效特征,他们综合反映了海杂波和含目标回波在功率、分形、几何、散射等方面的差异性。其次,联合所有特征构建一个高维特征空间,并将传统二元检测问题转换为高维特征空间中的两分类问题。随后,从结构和参数两个层面,设计一种改进1D-AlexNet的两分类器,用于检测小目标。在结构层面,将传统二维AlexNet模型降低到一维并进行层数精简化,从而加快模型的训练速度。在参数层面,引入具有自适应调整斜率的激活函数,保证模型的稳定性。同时,将幂指数衰减函数替代固定学习率,进一步提高模型的分类精度。最后,通过IPIX实测数据验证,结果表明:相对现有的特征检测器,所提出的检测器具有最佳的检测性能,并在复杂的杂波环境下仍能保持稳健性。此外,改进后的分类器结构简单,训练速度快,有望应用于实际雷达快速探测中。
Currently,detecting small targets on sea surfaces have become the focus of several researchers,as it is challenging to detect them using marine radars.Because of their low signal-to-clutter ratio(SCR)and weak maneuverability,traditional existing detectors often fail to detect small targets,resulting in severe performance loss,low detection probability,and high false alarm rate.To effectively detect small targets,marine radars often observe interesting areas with increased time observation,thereby improving the SCR of target returns.Full polarization radars with a new radar system can ensure sufficient observation time and further expand the dimension of information.Therefore,a new detec‐tion method via a modified 1D-AlexNet model is proposed in this paper,where information from received returns in full polarization radars is fully exploited to comprehensively improve the detection ability of small sea-surface targets.First,twenty-four features are extracted from the time,frequency,time-frequency,and polarization domains,reflecting the obvious differences between sea clutter and returns with target in terms of intensity,fractal,geometry,and physical scattering.Second,all features are combined to construct a high-dimensional feature space,and the traditional binary detection problem can be transformed into a binary classification problem in the high-dimensional feature space.Third,a modified 1D-AlexNet classifier is designed from the perspectives of structure and parameters.At the structural level,the traditional two-dimensional AlexNet model is reduced to one dimension,and the number of layers is finely simpli‐fied to accelerate the training speed.At the parameter level,an activation function with adaptive slope adjustment is in‐troduced to ensure the stability of the model in training.Meanwhile,classification accuracy of the model is further im‐proved by replacing the fixed learning rate with the exponential decay function.Finally,by using the open and recog‐nized IPIX measured datasets,experimental results show that the proposed detector can attain optimum performance in comparison with the existing several detectors and can still guarantee robust performance in complicated and various clutter environments.As a result,the improved 1D-AlexNet classifier has a simple structure and fast training speed and can be applied in practical radar rapid detection.
作者
施赛楠
姜苏桐
SHI Sainan;JIANG Sutong(School of Electronics&Information Engineering,Nanjing University of Information Science&Technology,Nanjing,Jiangsu 210044,China;Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET),Nanjing University of Information Science&Technology,Nanjing,Jiangsu 210044,China)
出处
《信号处理》
CSCD
北大核心
2024年第6期1098-1110,共13页
Journal of Signal Processing
基金
国家自然科学基金(62201184)
江苏省“双创团队”资助。
