A Hankel-based singular vector source enumeration for low signal-to-noise ratio

Abstract

Most existing source enumeration methods provide a satisfactory performance in high or middle signal-to-noise ratio (SNR), but almost lose effectiveness at low SNR. This paper proposed a source enumeration method based on squared Euclidean norm of the vector, the multiplication of the steering matrix and the left singular value vector of Hankel matrices for low SNR, which introduces the construction of abundant Hankel matrices with the different reference signals and changeable dimensions, and takes advantage of orthogonality of signal and noise, then employs the spatial smoothing scheme to dispose the dimension mismatched problem. Simulations validate the superiority of the proposed approach over the Akaike information criterion(AIC) and the minimum description length(MDL) for both coherent and non-coherent signals in terms of low SNR situation.