A class of high-resolution DOA estimation algorithms based on hyper-beamforming

In response to the demand for enhanced resolving power in direction-of-arrival (DOA) estimation algorithms and reduced background levels of spatial spectra, we propose a new class of algorithms based on hyper-beamforming (HBF). This class includes three specific algorithms: hyper-beam minimum-variance-distortionless-response (HMVDR), deconvolved hyper-beamforming (DHBF), and deconvolved HMVDR (DHMVDR). The HMVDR algorithm leverages the phase differences between the outputs from the left and right subarray MVDR algorithms and employs hyper-beam operations to construct a weighting factor that achieves high resolution. Both the DHBF and DHMVDR algorithms develop convolution models for the HBF and HMVDR, then apply the Richardson-Lucy method to deconvolve beams and extract the spatial spectra. The paper outlines the detailed steps and principles that underlie the high-resolution performance of these algorithms and examines the impact of the hyper-beam index on each. Through simulation results and the water tank experiment, we demonstrate that these algorithms not only lower the background level of spatial spectra and reduce the beamwidth of the main lobe but also exhibit superior DOA resolution.