摘要
Array imperfections will lead to serious performance degradation of the deep neural network(DNN)based direction of arrival(DOA)estimation in the low earth orbit(LEO)satellite communication by producing a mismatch between inference data and training data.In this paper,we propose a lightweight deep learning-based algorithm for array imperfection correction and DOA estimation.By preprocessing the covariance matrix of the array antenna outputs to the image,the array imperfection correction and DOA estimation problems are correspondingly converted into the image-to-image transformation task and image recognition task.Furthermore,for the deployment of real-time DNN-based DOA estimation on the resource-constrained edge system,generative adversarial network(GAN)model compression is applied to obtain a lightweight student generator of Pix2Pix for array imperfection correction.The Mobilenet-V2 is then used to extract the DOA information from the covariance matrix image.Simulations results demonstrate that the DOA estimation performance is significantly improved through the array imperfection correction.The proposed algorithm also better satisfies the real-time demand with decreased inference time on the resource-constrained edge system.
基金
supported in part by the National Natural Science Foundation of China(NSFC)under Grant 61971379
in part by the Key Research and Development Program of Zhejiang Province under Grant 2020C03100
in part by the Leading Innovative and Entrepreneur Team Introduction Program of Zhejiang under Grant 2018R01001
in part by the Fundamental Research Funds for the Central Universities under Grant 226202200096
in part by the Program of Innovation 2030 on Smart Ocean in Zhejiang University under Grant 129000*194232201.
