A Lightweight Deep Learning-Based Algorithm for Array Imperfection Correction and DOA Estimation

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.

DOA Estimation Based on Root Sparse Bayesian Learning Under Gain and Phase Error

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.