事件信号估计的MCA学习算法综述
MCA Learning Algorithm for Incident Signals Estimation: A Review
摘要：
近年来，在信号处理的文献中，有许多关于自适应子空间滤波的研究。它们中的大多数涉及跟踪由对应于信号加噪声数据的协方差矩阵的特征值的特征向量所构成的信号子空间。次成分分析（Minor Component Analysis，MCA）是一种重要的分析工具，在电信、天线阵列处理、统计参数估计等领域有着广泛的应用。作为一种重要的特征提取技术，MCA是一种提取协方差矩阵最小特征值所对应的特征向量的统计方法。本文提出了一种MCA学习算法，用于从输入信号中提取次要分量，并给出了学习率参数，该参数直接影响权向量的收敛性，影响误差水平，从而保证算法的快速收敛。执行MCA以确定估计的DOA。仿真结果将被提供来说明所取得的理论结果。
英文摘要：
Recently there has been many works on adaptive subspace filtering in the signal processing literature. Most of them are concerned with tracking the signal subspace spanned by the eigenvectors corresponding to the eigenvalues of the covariance matrix of the signal plus noise data. Minor Component Analysis (MCA) is important tool and has a wide application in telecommunications, antenna array processing, statistical parametric estimation, etc. As an important feature extraction technique, MCA is a statistical method of extracting the eigenvector associated with the smallest eigenvalue of the covariance matrix. In this paper, we will present a MCA learning algorithm to extract minor component from input signals, and the learning rate parameter is also presented, which ensures fast convergence of the algorithm, because it has direct effect on the convergence of the weight vector and the error level is affected by this value. MCA is performed to determine the estimated DOA. Simulation results will be furnished to illustrate the theoretical results achieved.
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