Abstract:

The existing sparse-recovery-based space-time adaptive processing(SR-STAP) method typically discretizes the angular Doppler plane into a multitude of grid points to generate a guidance dictionary.However,when these methods are employed for clutter suppression in bistatic airborne radars,they would encounter the issue of grid point mismatch,which significantly impairs the algorithm performance.In response to this problem,this paper presents an innovative approach using the atomic norm minimization(ANM) for clutter suppression in bistatic airborne radars.Unlike traditional methods,the ANM operates in the continuous domain without the need to generate a discrete grid matrix.Leveraging the positive semi-definiteness,block-Toplitz prosperity and low-rank nature of the clutter covariance matrix(CCM),the alternating direction multiplier method(ADMM) is used to iteratively solve the ANM problem,leading to the accurate estimation of the clutter subspace.Subsequently,the CCM is directly computed through eigen decomposition,improving the clutter suppression performance.Simulation results indicate that the proposed algorithm circumvents the grid-mismatch problem,achieves a more precise CCM estimation,and outperforms convolutional sparse recovery methods in terms of clutter suppression performance,particularly with fewer training samples.

Key words: airborne bistatic radar, clutter suppression, sparse recovery, atomic norm minimization