异常值个数未知下辅助数据自适应筛选方法  

Adaptive Screening Approach of Training Data with an Unknown Number of Outliers

作　　者：简涛 马颖亮 王海鹏 郭磊 魏广芬[3] JIAN Tao;MA Yingliang;WANG Haipeng;GUO Lei;WEI Guangfen(Research Institute of Information Fusion,Naval Aviation University,Yantai 264001,China;PLA unit 92830,Haikou 570100,China;School of Information and Electronic Engineering,Shandong Technology and Business University,Yantai 264005,China)

机构地区：[1]海军航空大学信息融合研究所,烟台264001 [2]92830部队,海口570100 [3]山东工商学院信息与电子工程学院,烟台264005

出　　处：《雷达学报（中英文）》2024年第5期1049-1060,共12页Journal of Radars

基　　金：国家自然科学基金(62471483,61971432);泰山学者工程专项经费资助(tsqn201909156);山东省高等学校青创科技支持计划(2019KJN031)。

摘　　要：在雷达目标多通道自适应检测场景下,诸多非均匀背景因素易导致异常值干扰,使得辅助数据独立同分布条件难以满足,已有辅助数据筛选方法往往假定异常值个数已知,在个数未知的情况下面临较大性能损失。为此,该文研究了异常值个数未知情况下辅助数据自适应筛选方法。首先,在杂噪协方差矩阵已知条件下,建立了异常数据集合的最大似然估计,基于广义内积对辅助数据进行初步排序,将异常数据集合的最大似然估计过程简化为异常值个数估计。其次,利用快速最大似然方法进行未知协方差矩阵估计,提出了未知异常值个数下辅助数据自适应筛选方法。进一步地,为降低异常值对初步排序性能的不利干扰,基于归一化采样协方差矩阵设计了归一化广义内积形式,并结合迭代估计方式,对前述辅助数据自适应筛选流程进行改进。仿真结果表明,与广义内积相比,采用归一化广义内积可获得更高的筛选精度,采用较小迭代次数即可获得稳定的归一化信干比改善;与已有辅助数据筛选方法相比,该文所提方法在异常值个数未知条件下具有更好的筛选性能。In multichannel adaptive radar target detection,diverse nonhomogeneous background factors can cause considerable outlier interference,making it challenging to meet the requirements of independent and identically distributed training data.Current methods for screening training data rely on prior knowledge of the number of outliers,often leading to poor performance in real-world scenarios where this number is usually unknown.This paper addresses these issues by focusing on adaptive training data screening when the number of outliers is unknown.First,the outlier set is estimated using maximum likelihood estimation,assuming known covariance matrices of clutter and noise.In particular,the training data is initially ranked based on the generalized inner product of each range cell data,approximately transforming the maximum likelihood estimation of the outlier set to the estimation of the number of outliers.Second,a fast maximum likelihood estimation algorithm is employed to calculate the unknown covariance matrix,and an adaptive screening approach is designed for scenarios with an unspecified number of outliers.Furthermore,to address the adverse effects of outliers on ranking performance,a normalized generalized inner product form is devised utilizing the normalized sampling covariance matrix.This form is subsequently incorporated into an iterative estimation procedure to improve the adaptive screening accuracy of training data.Simulation results demonstrate that the screening accuracy of the normalized generalized inner product exceeds that of the generalized inner product.Moreover,through even a small number of reiterations,maintaining a consistent enhancement in terms of the Normalized Signal-to-Interference Ratio(NSIR)is still possible.Compared with existing methods,the proposed algorithm considerably improves screening performance,especially when the number of outliers is unknown.

关 键 词：自适应目标检测 异常值个数 自适应筛选 似然函数 归一化广义内积 

分 类 号：TN958[电子电信—信号与信息处理]