Narrow Results

A robust algorithm for source number estimation based on the formation of the Hankel covariance matrix is presented. First, multiple data snapshots are taken successively from overlapped subarrays in a way similar to the forward spatial smoothing method to construct the special Hankel covariance matrix and for the total number of subarrays, these special covariance matrices are generated. Then, the average of these matrices is employed in singular value decomposition to generate the corresponding eigenvalues. Finally, the resulting eigenvalues are evaluated via the rule presented in this paper as the Moving Gradient Criterion (MGC) to estimate the number of sources by detection of the largest singular values. The greatest difference between the proposed algorithm and the other conventional methods is the form of the covariance matrix with the observed signal that can handle both non-coherent as well as fully coherent sources. Also, the proposed MGC rule adopted with this form of the covariance matrix is the strength of this work. Numerical simulations demonstrate the high superiority of the proposed approach over the competing methods such as MDL, AIC, SORTE, RAE and MSEE methods, especially in the cases of very closely spaced sources, low SNR values, low sensors number and low snapshots number.

Traditional beamforming algorithms have relatively poor performance in conditions of small snapshot numbers, high signal-to-noise-ratio and coherent sources. To address this issue, an oblique projection-based beamforming algorithm is modified in this paper. In this algorithm, the oblique projector is utilized to eliminate the noise of the array input data and enhance the robustness of algorithm. Furthermore, the transformation-based linear constraint matrix may eliminate the interferences, and Chebyshev window function is utilized to suppress the side-lobe level. Simulation results demonstrate that the modified algorithm has good robustness and performance.