A Real Aperture Radar Angle Super-resolution Method based on Iterative Reweighted Norm with TSVD Initialization

摘要

In real aperture radar forward-looking imaging, the angle super-resolution problem can be converted to the L1 norm minimization problem because the sparse characteristics of the target are considered. The iterative reweighted norm (IRN) methods are widely employed to solve L1 norm minimization issue. However, such methods exhibit the drawback of being sensitive to noise. In order to settle the trouble, this paper proposes an IRN with truncated singular value decomposition (TSVD) initialization method, which weakens the noise by the TSVD method during the initialization process and avoids noise amplification. Compared with the conventional L2 norm initialization and least squares (LS) initialization, this method shows better angle super-resolution performance in low signal to noise ratio (SNR) condition. The simulation results indicate the effectiveness of the proposed method.