Atomic Norm Based Localization of Far-Field and Near-Field Signals with Generalized Symmetric Arrays

TL;DR

This paper introduces a novel localization approach for mixed far-field and near-field signals using generalized symmetric arrays, leveraging atomic norm minimization and high-order statistics for improved accuracy without discretization.

Contribution

The paper presents a new localization method applicable to various sparse linear arrays, combining atomic norm minimization with high-order statistics for joint DOA and range estimation.

Findings

Effective localization of mixed FF and NF sources demonstrated

Method outperforms traditional ULA-based approaches

Applicable to diverse array geometries

Abstract

Most localization methods for mixed far-field (FF) and near-field (NF) sources are based on uniform linear array (ULA) rather than sparse linear array (SLA). In this paper, we propose a localization method for mixed FF and NF sources based on the generalized symmetric linear arrays, which include ULAs, Cantor array, Fractal array and many other SLAs. Our method consists of two steps. In the first step, the high-order statistics of the array output is exploited to increase the degree of freedom. Then the direction-of-arrivals (DOAs) of the FF and NF sources are jointly estimated by using the recently proposed atomic norm minimization (ANM), which belongs to the gridless super-resolution method since the discretization of the parameter space is not required. In the second step, the ranges are given by MUSIC-like one-dimensional searching. Simulations results are provided to demonstrate the advantages of our method.