Estimating direction-of-arrival via time-frequency signal subspace fitting

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.