High-frequency distributed super nested arrays based on Hermitian Toeplitz matrix reconstruction

Abstract

In array processing, mutual coupling between antenna elements is inevitable, which has an adverse effect on the estimation of parameters. Meanwhile, the expensive computational requirements are also not conducive to the real-time processing. In this paper, a new Hermitian Toeplitz matrix reconstruction algorithm suitable for distributed sparse arrays with contiguous difference co-array is proposed. The computational complexity of the new algorithm is quantitatively analyzed, which is much lower than that of the spatial smoothing algorithm. Then, a new array called distributed super nested arrays (DSNA) is proposed to reduce the mutual coupling between antenna elements. In contrast to super nested arrays (SNA) and nested arrays (NA), the proposed array can further improve the accuracy of direction-of-arrival (DOA) estimation by using the multiscale estimation of signal parameters via rotational invariance techniques (ESPRIT) algorithm. In high-frequency surface wave radar (HFSWR), the proposed array and algorithm can effectively reduce the mutual coupling in a wide frequency range, improve DOA estimation performance, significantly increase the number of detectable source signals, and reduce computational complexity. Numerical simulations demonstrate the superiority of DSNA using the proposed Hermitian Toeplitz matrix reconstruction algorithm.