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 feature.The fused clutter features are then used to construct a three-dimensional convex hull decision region,where target presence is determined by assessing whether the detection unit's feature point lies within this region.Detection results are compared with commonly used feature detection methods.Additionally,the study evaluates the boundary performance of the proposed method and contrasts it with the traditional energy-domain CFAR method,providing a comprehensive analysis of its usability and effectiveness.Results and Discussions The proposed method achieves the following results:(1)For clutter data,the temporal fusion algorithm reduces data variance by an average of 0.0245 compared to no temporal fusion and by 0.0035 compared to the original temporal fusion algorithm.For target data,it reduces data variance by an average of 1.1266 compared to no temporal fusion and by 0.179 compared to the original temporal fusion algorithm.(2)The Bhattacharyya distance of the proposed temporal fusion algorithm improves by an average of 0.2373 compared to no temporal fusion and by 0.1093 compared to the original temporal fusion algorithm.Under VV polarization,the Bhattacharyya distance improves by an average of 0.2199 compared to no temporal fusion and by 0.0908 compared to the original temporal fusion algorithm.(3)The proposed method outperforms other feature detectors in detection performance by effectively utilizing temporal information from historical frames,thereby enhancing the echo information used.Compared to energy-domain CFAR methods,it maintains a strong competitive advantage.Conclusions This study presents innovative solutions to two key challenges in existing sea clutter feature modeling and fusion methods.First,to address the time-varying nature of sea clutter features,a model-stable modified Burg method is proposed for Autoregressive(AR)feature modeling.This approach enables adaptive adjustment of model pole distribution,improving the accuracy of one-step sea clutter feature predictions and simplifying model order estimation.Second,to enhance the utilization of inter-frame temporal information during feature fusion,a dynamic weighted fusion algorithm is introduced to integrate predicted and observed features.This method reduces the variance of fused features and fully exploits historical temporal information.Validation using the IPIX dataset and the shared dataset from the Naval Aeronautical University demonstrates that the fused features obtained through these methods exhibit improved separability compared to the original features,significantly enhancing detector performance.
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)
出处
《电子与信息学报》
北大核心
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)。
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
Small target detection
Sea clutter
Temporal feature information
Modified burg
Dynamic weighting
