Fast Wideband Beamforming Using Convolutional Neural Network
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Published:2023-01-25
Issue:3
Volume:15
Page:712
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ISSN:2072-4292
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Container-title:Remote Sensing
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language:en
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Short-container-title:Remote Sensing
Author:
Wu Xun1, Luo Jie1, Li Guowei1, Zhang Shurui1ORCID, Sheng Weixing1
Affiliation:
1. School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
Abstract
With the wideband beamforming approaches, the synthetic aperture radar (SAR) could achieve high azimuth resolution and wide swath. However, the performance of conventional adaptive wideband time-domain beamforming is severely affected as the received signal snapshots are insufficient for adaptive approaches. In this paper, a wideband beamformer using convolutional neural network (CNN) method, namely, frequency constraint wideband beamforming prediction network (WBPNet), is proposed to obtain a satisfactory performance in the circumstances of scanty snapshots. The proposed WBPNet successfully estimates the direction of arrival of interference with scanty snapshots and obtains the optimal weights with effectively null for the interference by utilizing the uniqueness of CNN to extract potential nonlinear features of input information. Meanwhile, the novel beamformer has an undistorted response to the wideband signal of interest. Compared with the conventional time-domain wideband beamforming algorithm, the proposed method can fast obtain adaptive weights because of using few snapshots. Moreover, the proposed WBPNet has a satisfactory performance on wideband beamforming with low computational complexity because it avoids the inverse operation of covariance matrix. Simulation results show the meliority and feasibility of the proposed approach.
Funder
National Natural Science Foundation of China Postgraduate Research & Practice Innovation 389 Program of Jiangsu Province
Subject
General Earth and Planetary Sciences
Reference46 articles.
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