基于特征时序性的海杂波环境小目标检测方法  

A Detection Method of Small Target in Sea Clutter Environment Based on Feature Temporal Sequence

作　　者：董云龙 罗霄 丁昊 王国庆 刘宁波 DONG Yunlong;LUO Xiao;DING Hao;WANG Guoqing;LIU Ningbo(Naval Aviation University,Yantai 264001,China)

机构地区：[1]海军航空大学,烟台264001

出　　处：《电子与信息学报》2025年第3期707-719,共13页Journal of Electronics & Information Technology

基　　金：国家自然科学基金(62388102,62101583)。

摘　　要：特征检测作为海杂波环境下小目标检测的有效手段,受到了广泛关注与深入研究。过去对特征的研究大多关注于当前帧,近年来使用帧间时序信息融合当前帧特征的方法也被提出并在检测方面取得一定效果。但该方法不能很好地适应具有时变性的海杂波数据,且仅采用静态加权算法融合特征,对历史帧信息的利用不够充分。针对上述问题,该文提出基于模型稳定的修正Burg方法进行特征自回归(AR)建模与一步预测,使模型能够自适应调整极点分布,提高了海杂波特征预测的准确性,并基于求解多变量极值问题提出了一种动态加权算法得到了最小方差的融合特征。该文结合IPIX数据集和海军航空大学共享数据集进行实验,利用相对平均幅度(RAA)、相对多普勒峰高(RDPH)、频域峰均值比(FPAR)3特征构建凸包检测器验证了所提方法的有效性。Objective Feature detection has become an effective approach for detecting small targets in sea clutter environments,attracting significant attention and research.Previous studies primarily focused on extracting differential features between targets and clutter from the current pulse frame for detection.Recent methods have integrated temporal information from multiple frames with current frame features,demonstrating improved detection performance.However,these methods rely on fixed-order Auto Regressive(AR)models,which do not effectively adapt to the time-varying nature of sea clutter.Moreover,the use of static weighting algorithms for feature fusion fails to account for clutter characteristics in the current scene,leading to suboptimal utilization of temporal information.To address these issues,this study proposes a feature AR modeling and one-step prediction method based on a model-stable modified Burg algorithm,enabling adaptive pole distribution adjustment and enhancing the accuracy of sea clutter feature prediction.Additionally,a dynamic weighting algorithm is developed by solving multivariable extreme value problems to obtain minimum variance fused features,fully leveraging historical frame temporal information and improving radar target detection performance.Methods This study employs a modified Burg method to predict sea clutter,incorporating a stability factor in the derivation of reflection coefficients to constrain the model's poles within the unit circle.This enhances model stability,improving its adaptability to the time-varying nature of sea clutter and increasing the accuracy of feature prediction.A dynamic weighting algorithm is introduced to adaptively adjust fusion weights based on data volatility around the current frame by solving a multivariable extremum problem,thereby minimizing the local variance of fused features.Temporal fusion is performed using the features Relative Average Amplitude(RAA),Frequency Peak to Average Ratio(FPAR),and Relative Doppler Peak Height(RDPH)to generate a fused fe

关 键 词：小目标检测 海杂波 特征时序信息 修正Burg方法 动态加权 

分 类 号：TN957.51[电子电信—信号与信息处理]