Full polarisation ISAR imaging based on joint sparse Bayesian compressive sensing

This study proposes a joint sparse algorithm based on Bayesian compressive sensing to improve full polarisation inverse synthetic aperture radar (ISAR) imaging performance. The proposed method not only uses the sparseness of each single channel polarisation, but also takes into account the correlation of amplitude information between single-polarised channels. Through the comprehensive use of single channel polarisation imaging results, a better full-polarisation imaging result is achieved. Simulation results are used to verify the effectiveness of the method.