Target Direction Agnostic Maximum SNR Beamformer: An Improved Interference Suppression Algorithm

Adaptive beamforming has become an important signal processing technique for suppression of interference/unwanted signals. Robust Adaptive beamforming (RAB) is a class of techniques that achieve interference suppression without distorting the beam in the region of interest. RAB techniques generally define a spatial sector of interest within which the target signal is expected. Further, it is assumed that no interference is present in the sector of interest. Such assumptions give rise to two main problems: 1) A mismatch in the assumption (or estimate) of target direction gives sub-optimal SNR improvement and 2) poor suppression of In-band interference (within the sector of interest). We address these issues in this work. We propose a novel RAB called Target Direction Agnostic Maximum Signal-to-Noise Ratio (TDA-MSNR) beamformer. We show that TDA-MSNR requires no prior knowledge of the target direction. Further, we demonstrate an improved in-band suppression performance compared to the other RAB techniques. We also show that algorithm is computationally efficient. Further, TDA-MSNR finds direct application to Space Time Adaptive Processing (STAP-Clutter suppression).