Bayesian Complex Amplitude Estimation and Adaptive Matched Filter Detection in Low-Rank Interference
Bayesian Complex Amplitude Estimation and Adaptive Matched Filter Detection in Low-Rank Interference
复制标题
低阶干扰中的贝叶斯复振幅估计和自适应匹配滤波器检测
DOI：
10.1109/tsp.2006.887151
复制
发表时间：
2007
影响因子：
5.4
通讯作者：
Zhang, Benhong
中科院分区：
文献类型：
--
作者：
Dogandzic, Aleksandar;Zhang, Benhong
我们提出了一种用于低阶干扰中复杂幅度估计的贝叶斯方法。我们假设接收到的信号遵循广义多元方差分析 (GMANOVA) 模式均值结构，并受到低秩空间相关干扰和白噪声的破坏。开发了迭代条件模式 (ICM) 算法，用于估计未知的复信号幅度以及干扰和噪声参数。我们还讨论了 ICM 算法的初始化，并提出了一种利用 ICM 估计结果的（非贝叶斯）自适应匹配滤波器（AMF）信号检测器。数值模拟证明了所提出方法的性能。
We propose a Bayesian method for complex amplitude estimation in low-rank interference. We assume that the received signal follows the generalized multivariate analysis of variance (GMANOVA) patterned-mean structure and is corrupted by low-rank spatially correlated interference and white noise. An iterated conditional modes (ICM) algorithm is developed for estimating the unknown complex signal amplitudes and interference and noise parameters. We also discuss initialization of the ICM algorithm and propose a (non-Bayesian) adaptive-matched-filter (AMF) signal detector that utilizes the ICM estimation results. Numerical simulations demonstrate the performance of the proposed methods.