A Space-Frequency Processor for Identifying and Isolating GNSS Signals Amidst Interference

A conventional Direction of Arrival (DoA) estimation algorithm at a GNSS (Global Navigational Satellite System) receiver equipped with a phased array of antennas cannot classify whether the estimated DoA corresponds to the desired GNSS satellite or an interfering source. Furthermore, the GNSS signal is often a low-power spread-spectrum signal that lies below the receiver noise floor. These limitations hinder the application of beamforming in the direction of the GNSS satellite while suppressing the interfering sources. To address this challenge, we introduce a novel two-dimensional (2D) space-frequency DoA estimator and beamformer. Our proposed approach (i) elevates the low-SNR (signal-to-noise ratio) GNSS signals above the noise floor, (ii) leverages the known spectral density of GNSS signals for DoA and FoA (frequency of arrival) estimation, and (iii) performs computationally-efficient spectral domain beamforming and interference nulling. We provide analysis and simulation results to validate the proposed concept.