A Modified Denoise Approach for UCA DOA Estimation in Low SNR Case

When the uniform circular array (UCA) is used to estimate the direction-of-arrival (DOA) of the coherent sources, it is necessary to transform the UCA data to the interpolated uniform linear array (ULA) data. Thus, the transformed array data can be applied to the spatial processing algorithm for the coherent sources such as the forward-backward smoothing algorithm. To select a more robust transformation matrix, a modified denoise approach for UCA estimation in low SNR case is proposed in this paper. First, a denoise method is investigated to maximize the SNR in the process of the interpolated transformation. The pseudo signal to noise ratio (PSNR) obtained from the eigenvalues of the forward-backward smoothing virtual covariance matrix estimate is used as the parameter of this maximization problem. Second, a modified maximization problem and its solution are presented to obtain more accurate estimates. The simulation results demonstrate the effectiveness of the proposed method, which improve the resolution ability and the estimation accuracy of the coherent sources in the low SNR case at the same time.