Mainlobe Jamming Suppression Method for Distributed Array Radar Based on Variational Sparse Bayesian Learning Jamming Estimation and Null Broadening Range-Angle Beamforming

Distributed array radar (DAR) expands the aperture of the array radar by adding multiple synchronized auxiliary arrays with the main array, thereby enhancing the ability to counter mainlobe jamming. However, the long baseline of DAR causes signal sources to fall into the near-field region. The coupling of range and angle parameters in the near-field signal model poses challenges to anti-jamming methods based on jamming parameter estimation and cancelation. To address this issue, this paper proposes a mainlobe jamming suppression method for DAR based on variational sparse Bayesian learning (SBL) jamming estimation and range-angle two-dimensional null broadening beamforming. To decouple the range and angle parameters in the near-field steering vector model, a variational grid optimisation nonuniform sparse recovery dictionary is designed. Afterwards, iterative-optimised variational SBL using prior information is performed to estimate the range-angle parameters of jammers accurately. Given potential estimation errors, two-dimensional null broadening beamforming based on steering vector perturbation is proposed to suppress jamming. Simulation and experimental results verify that the proposed method can effectively reduce the computational complexity of sparse recovery, obtain more accurate jamming parameters, and achieve higher output signal-to-interference-plus-noise ratio (SINR) for jamming suppression.