Two-Stage Spatial Whitening and Normalized MUSIC for Robust DOA Estimation of GNSS Signals Under Jamming

The estimation of the direction of arrival (DOA) of navigation signals is a critical function of a global navigation satellite system (GNSS) array receiver for applications such as attitude determination, spoofing detection, and blind adaptive beamforming. However, high-power GNSS jamming poses significant challenges to accurate DOA estimation. This paper presents spatial whitening and normalized multiple signal classification (SWN-MUSIC), an enhanced two-stage MUSIC algorithm for GNSS signal DOA estimation under jamming. In the first stage, inspired by minimum variance distortionless response (MVDR) beamforming, we use a spatial whitening matrix for matrix filtering to achieve jamming suppression. In the second stage, an approach accounts for matrix filtering effects when computing the MUSIC pseudospectrum. Compared to prior GNSS DOA methods under jamming, our SWN-MUSIC shows improved accuracy, particularly when the DOA of jamming and signals are closely aligned. Additionally, we develop a reduced-dimension SWN-MUSIC (RD-SWN-MUSIC) algorithm to lower the complexity of applying SWN-MUSIC for two-dimensional DOA estimation using uniform rectangular arrays (URA) under jamming. Compared to standard reduced-dimension MUSIC, our RD-SWN-MUSIC avoids unnecessary precision loss by performing separate one-dimensional parameter searches for each DOA parameter. Numerical simulations indicate that RD-SWN-MUSIC closely approaches the Cramer-Rao lower bound (CRLB), with significantly lower complexity than classical MUSIC and its previously proposed versions.