Localization of far-field and near-field signals with mixed sparse approach: A generalized symmetric arrays perspective

Abstract

Most existing methods for mixed far-field (FF) and near-field (NF) sources localization are based on uniform linear arrays (ULAs) or some special sparse linear arrays (SLAs) such as symmetric nested arrays. How to employ other linear arrays for mixed sources localization is still unknown. In this paper, we propose a generalized symmetric linear array framework which unifies all the symmetric ULAs or SLAs including the symmetric nested arrays, cantor array, fractal array and many other symmetric SLAs for mixed sources localization. To increase the degrees-of-freedom (DoFs) of these arrays, we utilize the high-order cumulant matrix of the array output from the coarray perspective. The atomic norm technique is employed for estimating the angles of the FF and NF sources from the gridless manner. The range information of the NF sources is obtained by applying the ℓ1-norm minimization technique to the covariance signal model. Our method can be applied to any ULAs or symmetric SLAs for mixed FF and NF sources localization with high estimation accuracy. By exploiting the coarray property, our method can locate more sources than sensors with proper arrays. Extensive simulations are carried out to show the effectiveness of our proposed generalized symmetric linear array framework and method.