A Radar Forward-Looking Super-Resolution Method Based On Singular Value Weighted Truncation

Abstract

The truncated singular value decomposition (TSVD) method has been applied to radar forward-looking imaging, however which suffers limited resolution. Especially under low signal to noise ratio (SNR) condition, there is a contradiction between keeping more singular values to improve resolution and suppressing noise amplification. In this paper, a method based on singular value weighted truncation is proposed to improve the resolution under low SNR condition. First, this paper analyses the essence of the conventional TSVD method. Then, the passage constructs a new singular value function to reserve more singular value on the original truncation parameter. Compared with the conventional TSVD method, the more singular values are retained which can improve the resolution under the premise of suppressing noise. Simulations demonstrate the effectiveness of the proposed method.