Most existing source enumeration techniques in sensor-based studies have a satisfactory performance in high or middle signal-to-noise ratio (SNR), but lose effectiveness at low SNR. After Hankel matrices with different referenced signals and changeable dimensions are constructed, a source enumeration method is proposed based on the squared Euclidean norm of the product vector of the steering matrix and left singular vectors of Hankel matrix for low SNR. The proposed method employs the blind beamforming technique to estimate the steering matrix and the spatial smoothing scheme to dispose the dimension mismatched problem. Simulations are performed to display the validation for the non-coherent and coherent source signals and better enumeration performance at low SNR, in comparison with the conventional Akaike information criterion and minimum description length methods.
['Hairong Zhang', 'Pan Jiang', 'Guijin Yao', 'Ling Li']
Source Enumeration Based on the Singular Vector of Hankel Matrix for Low Signal-to-Noise Ratio