发表机构
University of Pisa; Università del Salento（比萨大学; 莱切大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出基于子空间的二阶检测架构，利用似然比检验和被动训练数据估计未知参数，在无结构干扰下有效检测距离扩展目标。
AI 中文摘要
本文提出了一种基于子空间的决策方案，用于在存在热噪声、杂波和有意干扰的情况下检测距离扩展目标。目标和有意干扰（即类噪声干扰机）的影响被限制在接收主数据向量的协方差矩阵中。干扰机也存在于估计杂波协方差矩阵所必需的辅助数据向量中。最后，系统在被动模式下获取的一组额外训练数据（因此不受目标和杂波回波污染）用于避免干扰机参数不可辨识的问题。检测问题通过推导基于似然比检验的架构来解决，其中未知的二阶参数被适当地估计。为此，定义了一个统一的理论框架，并为估计过程提供了几何解释。性能评估（也与自然竞争方法进行了比较）突出了所提出方案的有效性。
英文摘要
This paper proposes a subspace-based decision scheme to detect range-spread targets in presence of thermal noise, clutter, and intentional interference. The target and intentional interference (i.e., noise-like jammer) effects are confined to the covariance matrix of the received primary data vectors. The jammer is also present in the secondary data vectors necessary to estimate the clutter covariance matrix. Finally, an additional set of training data, acquired by the system in passive mode and, hence, not contaminated by target and clutter echoes, is used to avoid the jammer parameters are unidentifiable. The detection problem is addressed by deriving an architecture based upon the likelihood ratio test where the unknown second-order parameters are suitably estimated. To this end, a unified theoretical framework is defined also providing a geometrical interpretation of the estimation procedures. The performance assessment, conducted also in comparison to a natural competitor, highlights the effectiveness of the proposed solution.
