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 betw...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.展开更多
The direction of arrival(DOA)is approximated by first-order Taylor expansion in most of the existing methods,which will lead to limited estimation accuracy when using coarse mesh owing to the off-grid error.In this pa...The direction of arrival(DOA)is approximated by first-order Taylor expansion in most of the existing methods,which will lead to limited estimation accuracy when using coarse mesh owing to the off-grid error.In this paper,a new root sparse Bayesian learning based DOA estimation method robust to gain-phase error is proposed,which dynamically adjusts the grid angle under coarse grid spacing to compensate the off-grid error and applies the expectation maximization(EM)method to solve the respective iterative formula-based on the prior distribution of each parameter.Simulation results verify that the proposed method reduces the computational complexity through coarse grid sampling while maintaining a reasonable accuracy under gain and phase errors,as compared to the existing methods.展开更多
基金supported in part by the National Natural Science Foundation of China(NSFC)under Grant 61971379in part by the Key Research and Development Program of Zhejiang Province under Grant 2020C03100+2 种基金in part by the Leading Innovative and Entrepreneur Team Introduction Program of Zhejiang under Grant 2018R01001in part by the Fundamental Research Funds for the Central Universities under Grant 226202200096in part by the Program of Innovation 2030 on Smart Ocean in Zhejiang University under Grant 129000*194232201.
文摘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National Natural Sci-ence Foundation of China(NSFC)(61971379)Key Research and Development Program of Zhejiang Province(2020C03100)+2 种基金Leading Innovative and Entrepreneur Team In-troduction Program of Zhejiang(2018R01001)Fundamental Research Funds for the Central Universities(226202200096)Program of Innovation 2030 on Smart Ocean in Zhejiang University(129000*194232201)。
文摘The direction of arrival(DOA)is approximated by first-order Taylor expansion in most of the existing methods,which will lead to limited estimation accuracy when using coarse mesh owing to the off-grid error.In this paper,a new root sparse Bayesian learning based DOA estimation method robust to gain-phase error is proposed,which dynamically adjusts the grid angle under coarse grid spacing to compensate the off-grid error and applies the expectation maximization(EM)method to solve the respective iterative formula-based on the prior distribution of each parameter.Simulation results verify that the proposed method reduces the computational complexity through coarse grid sampling while maintaining a reasonable accuracy under gain and phase errors,as compared to the existing methods.
