Coupling-Informed Data-Driven Scheme for Joint Angle and Frequency Estimation in Uniform Linear Array With Mutual Coupling Present

Abstract

This article proposes a novel coupling-informed data-driven algorithm tailored for the concurrent estimation of frequency and angle within a uniform linear array (ULA), while addressing the complicating influence of mutual coupling. Leveraging the hybrid dynamic mode decomposition (DMD) methodology, termed as averaged DMD, we incorporate moving average techniques to achieve effective denoising. The averaged DMD further decomposes the received signal into eigenvalues and corresponding eigenvectors. The frequency information is derived from the eigenvalues and the corresponding eigenvectors represent the steering vectors of sources. Subsequently, mutual coupling is informed into the calibration of the steering vector for each source. Specifically, the calibration of corresponding eigenvectors leverages the inverse of the mutual coupling matrix, i.e., Toeplitz matrix, acquired through Schur decomposition. Then, the calibrated steering vectors facilitate the estimation of angles. The decomposition results of our proposed method reveal a significant one-to-one correspondence between eigenvectors and eigenvalues, enabling automatic pairing of estimated frequencies and angles. Several numerical examples demonstrate the effectiveness and robust anti-noise properties of the proposed method, especially in scenarios where mutual coupling has a significant impact. Hence, our work contributes to the advancement of signal processing techniques in ULA applications, offering a promising avenue for enhanced performance in practical communication and radar systems.