Antenna Pattern Design of Azimuth Super-Resolution Imaging for Dual-Channel Real Aperture Radar

DOI number:10.1109/JSEN.2025.3614305

Affiliation of Author(s):中国科学技术大学信息学院

Teaching and Research Group:电子工程与信息科学系

Journal:IEEE Sensors Journal (Early Access)

Key Words:Azimuth resolution, antenna pattern design, particle swarm optimization (PSO), random forest, real aperture radar (RAR).

Abstract:The azimuth resolution of real aperture radar (RAR) is limited by the effective beamwidth of the antenna. Based on the convolution relationship between target scatterings and the antenna pattern in the traditional model, the azimuth resolution can be improved by deconvolution methods. However, due to the low pass nature of the antenna pattern, the radar observation matrix is of great ill-conditioning, thus, direct deconvolution is sensitive to noise which will be detrimental to azimuth resolution enhancement. In this paper, a RAR two-channel antenna pattern design method is proposed where we mitigate the ill-conditioning of the radar observation matrix from the perspective of designing the antenna pattern, while the traditional methods improve the super-resolution capability with algorithms. Firstly, we elicit the reconstruct probability to analyze the reconstruction performance, however, which is not suitable for the antenna pattern design. Hence, inspired by the deconvolution theory, we propose four metrics to reflect the ill-conditioning of the radar observation matrix and use them to characterize the reconstruct probability with the random forest algorithm. Besides, we use their relationship as the objective function to obtain the optimally-designed antenna pattern with the particle swarm algorithm. Considering the feasibility of the antenna pattern, we also approximate the theoretical solution with beam assignment. Simulations verify the proposed method for robustness enhancement with different algorithms both on the traditional convolution model and the Doppler-convolution model, and higher reconstruct probability, lower Mean Squared Error(MSE), fewer entropy and higher target-to-clutter ratio(TCR) are also demonstrated in real scene from publicly available dataset.

First Author:Zhipeng Yang

Co-author:Zhiping Yin,Falin Liu

Indexed by:Journal paper

Correspondence Author:Guanghua Lu

Document Code:10.1109/JSEN.2025.3614305

Discipline:Engineering

Document Type:J

Volume:Online

Issue:Online

Page Number:1-14

Translation or Not:no

Date of Publication:2025-10-06