Noise reduction in ISAR imaging of UAVs using weighted atomic norm minimization and 2D-ADMM algorithm

TL;DR

This paper introduces a novel noise reduction algorithm for ISAR imaging of UAVs that enhances image resolution in noisy conditions by employing weighted atomic norm minimization and a 2D-ADMM optimization method.

Contribution

The paper proposes the FRAND algorithm with weighted atomic norm minimization and 2D-ADMM to improve noise reduction and resolution in UAV ISAR images, especially at low SNRs.

Findings

FRAND outperforms MUSIC, Cadzow, and SL0 methods in low SNR conditions.

Simulation results show improved MSE, PSNR, and SSIM metrics.

The method effectively enhances 2D resolution of UAV images under noisy scenarios.

Abstract

The effect of noise on the Inverse Synthetic Aperture Radar (ISAR) with sparse apertures is a challenging issue for image reconstruction with high resolution at low Signal-to-Noise Ratios (SNRs). It is well-known that the image resolution is affected by the bandwidth of the transmitted signal and the Coherent Processing Interval (CPI) in two dimensions, range and azimuth, respectively. To reduce the noise effect and thus increase the two-dimensional resolution of Unmanned Aerial Vehicles (UAVs) images, we propose the Fast Reweighted Atomic Norm Denoising (FRAND) algorithm by incorporating the weighted atomic norm minimization. To solve the problem, the Two-Dimensional Alternating Direction Method of Multipliers (2D-ADMM) algorithm is developed to speed up the implementation procedure. Assuming sparse apertures for ISAR images of UAVs, we compare the proposed method with the MUltiple SIgnal Classification (MUSIC), Cadzow, and SL0 methods in different SNRs. Simulation results show the superiority of FRAND at low SNRs based on the Mean-Square Error (MSE), Peak Signal-to-Noise ratio (PSNR) and Structural Similarity Index Measure (SSIM) criteria.