Adaptive radar detection of distributed targets in homogeneous and partially homogeneous noise plus subspace interference
Adaptive radar detection of distributed targets in homogeneous and partially homogeneous noise plus subspace interference
复制标题
DOI：
10.1109/tsp.2006.888065
复制
发表时间：
2007-04-01
影响因子：
5.4
通讯作者：
Ricci, Giuseppe
中科院分区：
文献类型：
--
作者：
Bandiera, Francesco;De Maio, Antonio;Ricci, Giuseppe
本文讨论了噪声加干扰中分布式目标的自适应雷达检测问题。在设计阶段，我们诉诸于GLRT或所谓的两步GLRT为基础的设计程序，并假设一组只有噪音的数据（所谓的二次数据）。已推导出检测算法，将对应于不同距离单元的噪声向量建模为独立的、零均值的、复正态的噪声向量，共享相同的协方差矩阵（同质环境）或相同的协方差矩阵，直到原始数据之间可能不同的（平均）功率水平，即，测试范围内的细胞，和次要的（部分均匀的环境）。性能评估已进行蒙特卡罗模拟，也在以前提出的检测算法相比，并确认新提出的有效性。
This paper addresses adaptive radar detection of distributed targets in noise plus interference assumed to belong to a known or unknown subspace of the observables. At the design stage we resort to either the GLRT or the so-called two-step GLRT-based design procedure and assume that a set of noise-only data is available (the so-called secondary data). Detection algorithms have been derived modeling noise vectors, corresponding to different range cells, as independent, zero-mean, complex normal ones, sharing either the same covariance matrix (homogeneous environment) or the same covariance matrix up to possibly different (mean) power levels between primary data, i.e., range cells under test, and secondary ones (partially homogeneous environment). The performance assessment has been conducted by Monte Carlo simulation, also in comparison to previously proposed detection algorithms, and confirms the effectiveness of the newly proposed ones.