Range profile similarity‐oriented method for radar imaging of moving target under low signal‐to‐noise ratio environment. Issue 10 (3rd June 2022)

• Record Type: Journal Article
• Authors: Xu, Xinbo; Su, Fulin; Jin, Xinfei; Gao, Jianjun
• Is Part Of: IET radar, sonar & navigation. Volume 16:Issue 10(2022)
• Page Start: 1602
• Page End: 1615
• Publication Date: 2022-06-03
• Subjects: Signal processing -- Periodicals; Radar -- Periodicals; Sonar -- Periodicals; Electronics in navigation -- Periodicals; Navigation -- Periodicals; 621.3848
• DOI: 10.1049/rsn2.12283
• Languages: English
• ISSNs: 1751-8784

Abstract

Abstract: Inverse synthetic aperture radar (ISAR) imaging is an advanced technique for reconstructing high‐resolution images of moving targets. However, noise interference degrades the readability of the ISAR images under low echo signal‐to‐noise ratio (SNR) environments. Furthermore, noise impairs the performance of ISAR motion compensation, which induces severe defocussing. Thus, a novel range profile similarity‐oriented (RAP‐SO) method with a two‐step denoising strategy is presented. First, attributed to the coherence between ISAR range profiles, the coherent weighted template (CWT) is proposed to preliminarily remove the noise in a high‐resolution range profile (HRRP) and restore the range profile similarity impaired by noise. In order to robustly complete range alignment under a low SNR environment, Keystone transform (KT) is utilised. Thereafter, the weighted template is constructed by virtue of coherent integration. The CWT adaptively gives different weights to HRRP signals and noise, which is called coarse denoising. Second, inspired by the similarities of range profiles, the minimum weighted nuclear norm (Min‐WNN) optimisation is proposed to further reduce the residual noise. The coarse denoised HRRPs are rearranged into an approximate low‐rank matrix. Subsequently, rank‐constraint optimisation on the HRRP matrix via minimising the weighted nuclear norm is performed, aiming to efficiently suppress the residual noise. This step is called fine denoising. As a result, ISAR image quality is significantly improved. Comprehensive experiments illustrate the effectiveness and superiority of the presented method.