Partially Constrained Adaptive Beamforming

Abstract

The ReIterative Super-Resolution (RISR) was developed based on an iterative implementation of the Minimum Mean Squared Error (MMSE) estimator. Here, a novel approach to direction of arrival estimation, partially constrained beamforming is introduced by building from existing work on the RISR algorithm. First, RISR is rederived with the addition of a unity gain constraint, with the result denoted as Gain Constrained RISR (GC-RISR), though this formulation exhibits some loss in resolution. However, by taking advantage of the similar structure of RISR and GC-RISR, they can be combined using a geometric weighting term $\alpha$ to form a partially constrained version of RISR, which we denote as PC-RISR. Simulations are used to characterize PC-RISR's performance, where it is shown that the geometric weighting term can be used to control the speed of convergence. It is also demonstrated that this weighting term enables increased super-resolution capability compared to RISR, improves robustness to low sample support for super-resolving signals with low SNR, and the ability to detect signals with an SNR as low as -10dB given higher sample support.