Super-resolution Doppler Beam Sharpening based on Online Tikhonov Regularization

Abstract

Doppler beam sharpening (DBS) is a crucial technique in radar imaging. However, the conventional DBS based on fast Fourier transform provides low azimuth resolution and high sidelobe level. Many of the current super-resolution DBS imaging methods are carried out to greatly improve the azimuth resolution. Nonetheless, these methods generally adopt a disposal of batch processing with high computational complexity, causing the insufficient of real-time capability. This paper proposes an online super-resolution DBS imaging approach based on Tikhonov regularization by adding the regularization term and utilizing matrix blocking. The current iterative estimation value of the scattering coefficient can be updated by the former iterative estimation value and the new data acquired from the current. The proposed method effectively improves the resolution with allocating total computation burden for each sampling interval, and possesses the particularly advantage of acquiring echo while processing imaging results, which is suitable for realtime continuous reconnaissance. Simulation results are given to demonstrate the effectiveness of the proposed method.