海军航空大学学报2024，Vol.39Issue(5)：622-632,11.DOI:10.7682/j.issn.2097-1427.2024.05.012
子空间不确定下多重假设AMF、Rao与Wald检测方法
Multiple Hypotheses AMF,Rao and Wald Detection Method Under Subspace Uncertainty
摘要
Abstract
Since the uncertainty in the size of the target multi-rank subspace will lead to multiple hypotheses detection,the traditional target adaptive binary detection method is no longer applicable.To address this issue,a multiple hypothesis AMF,Rao and Wald detection method under subspace uncertainty is proposed.Firstly,based on the Kullback-Leibler in-formation criterion,a target detection model under multiple assumptions in the target multi-rank subspace is established.Then,based on the AMF,Rao and Wald detection criteria,multiple hypotheses detectors are designed,and the unknown parameters are optimized and the penalty term is calculated.Finally,the performance of the proposed detectors are veri-fied by simulation experiments,and the influence of the penalty term on the performance of each detector is analyzed.The experimental results show that compared with the traditional detector,the proposed detectors have better detection performance under certain conditions.关键词
自适应目标检测/多秩子空间/多重假设检测/模型阶次选择Key words
adaptive target detection/multi-rank subspace/multiple hypotheses detection/model order selection分类
信息技术与安全科学引用本文复制引用
田晗,张宇,许姗姗,高永婵,许智文..子空间不确定下多重假设AMF、Rao与Wald检测方法[J].海军航空大学学报,2024,39(5):622-632,11.基金项目
国家自然科学基金(62371379) （62371379）
