DOI码：10.1109/JSEN.2025.3614305

所属单位：中国科学技术大学信息学院

教研室：电子工程与信息科学系

发表刊物：IEEE Sensors Journal (Early Access)

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

摘要：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.

第一作者：Zhipeng Yang

合写作者：Zhiping Yin,Falin Liu

论文类型：期刊论文

通讯作者：Guanghua Lu

论文编号：10.1109/JSEN.2025.3614305

学科门类：工学

文献类型：J

卷号：Online

期号：Online

页面范围：1-14

是否译文：否

发表时间：2025-10-06