时频子空间拟合波达方向估计  

Estimating Direction-of-Arrival Via Time-Frequency Signal Subspace Fitting

作　　者：金梁[1] 殷勤业[2] 李盈[2] 

机构地区：[1]解放军信息工程大学,郑州450002 [2]西安交通大学信息工程研究所,西安710049

出　　处：《电子学报》2001年第1期71-74,共4页Acta Electronica Sinica

基　　金：高等学校骨干教师资助计划!(No .GJ 0 0 0 80 34);国家自然科学基金!(No .69872 0 2 9);国家"863"计划!(No.863 31 7 0 3 0 1 0 5

摘　　要：本文提出了一种基于信号空时特征结构的时频子空间拟合方法 ,利用双线性时频分布构造时频相关矩阵Cx 代替传统的阵列相关矩阵Rx,通过Cx 的特征分解实现了信号子空间与噪声子空间的分离 .该方法在空域和二维时频域同时进行处理 ,能够区分具有不同时频特征的信号 ,既适用于平稳信号的场合又适用于时变、非平稳信号的情形 ,属于空时多维处理的范畴 .可以证明 ,基于平稳信号假设的经典子空间方法是该方法的低维特例 .由于包含了时变滤波的过程 ,因此该方法具有信号选择性以及抗干扰和抗噪声的能力 .仿真结果证实了该方法的有效性 .Based on the decomposition of the space-time eigenstructure of signals, a novel time-frequency signal subspace fitting (TF-SSF) method is proposed to estimate the DOA of signals. Through the bi-linear Cohen class time-frequency distribution, the time-frequency correlation matrix Cx is constructed to replace the traditional correlation matrix Rx. Accordingly the signal subspace and noise subspace are separated with the eigen-decomposition of Cx. Because the observed data are processed in spatial domain and 2-D time-frequency domain simultaneously, the method can separate the signals that have different time-frequency signatures and is suitable for both stationary and nonstationary signals, while the traditional subspace methods have to assume that the signal is stationary. It is also proven herein that the traditional subspace methods is the special case of the TF-SSF method. Furthermore, with the time-varying filtering available, the method has the signal-selectivity and is capable of suppressing interference and noise that are difficult to handle in time or frequency domain only. The simulation results show the effectiveness of the method.

关 键 词：阵列处理 波达方向 时频 子空间拟合 信号处理 

分 类 号：TN911.7[电子电信—通信与信息系统]