Gradient Descent-Based Direction-of-Arrival Estimation for Lens Antenna Array

Abstract

In this letter, we investigate a novel optimization approach to direction-of-arrival (DoA) estimation for a lens antenna array. Inspired by a property of the sinc function and ${\ell _{2}}$-norm optimization, we develop the gradient descent-based spatial spectrum reconstruction (GD-SSR) to estimate the DoAs based on the sum signal covariance vector (SSCV). Our proposed algorithm does not require a priori knowledge of signal number and has a lower complexity compared with existing techniques while achieving a better estimation performance, even in a low-SNR regime. In addition, the proposed model does not require any pretraining process as prior learning-based methods. The simulation results show that our scheme not only outperforms other techniques but also resolves the angular ambiguity problem