Performance improvement for wideband DOA estimation with white noise reduction based on uniform linear arrays

Abstract

A method is proposed for reducing the effect of white noise in wideband uniform linear arrays via a combination of a judiciously designed transformation followed by highpass filters. The reduced noise level leads to a higher signal to noise ratio for the system, which can have a significant effect on the performance of various direction of arrival (DOA) estimation methods. As a representative example, the compressive sensing-based wideband DOA estimation method is employed here to demonstrate the improved estimation performance, this is confirmed by simulation results.

White noise reduction for wideband beamforming based on uniform rectangular arrays

“…However, in practice, the TSNR improvement will be less than that, due to the limited number of sensors in the URA. This TSNR improvement is less than the 3dB improvement which was achieved for ULAs using a 1D unitary transformation as in our previous work [4]. In the next section, as an alternative approach, we adapt the noise reduction method for ULAs to be applicable to URAs, in order to achieve a better output TSNR.…”

“…FOR URAS WITH A 1D TRANSFORMATION In this approach, the method developed for ULAs in [4] is adapted for the new URA structure. Each column of the sensors of a URA is actually a ULA.…”

“…The highpass filtering effect of the transformation for ULAs has been shown in [4]. Because of the highpass filtering effect of the transformation on the directional signal, the output spectrum of the directional signal part of q m,l [n] corresponding to the m-th row vector of A is highpass filtered.…”

“…In our previous work [4], a method was developed for reducing the effect of white noise in wideband uniform linear arrays (ULAs) via a combination of a judiciously designed transformation followed by highpass filters to improve the performance for wideband direction of arrival (DOA) estimation. Here we extend this idea to the case of uniform rectangular arrays (URAs) [5], [6], [7].…”

“…The first method is based on a twodimensional (2D) transformation. The second method is an adaptation of our previous work for ULAs [4], which is based on one-dimensional (1D) transformation of the signals received by the URA. The transformations must be invertible and ideally, unitary.…”