FFT-Enhanced Low-Complexity Near-Field Super-Resolution Sensing

TL;DR

This paper introduces an FFT-enhanced super-resolution sensing algorithm for near-field source localization that reduces computational complexity while accurately estimating both angle and range of sources.

Contribution

It proposes a novel combination of FFT-based spectral pattern construction and 2D MUSIC for efficient near-field source localization with high resolution.

Findings

Significantly reduces computational complexity of 2D spectrum peak searches.

Achieves high-resolution localization of near-field sources.

Demonstrates effectiveness through numerical simulations.

Abstract

In this letter, a fast Fourier transform (FFT)-enhanced low-complexity super-resolution sensing algorithm for near-field source localization with both angle and range estimation is proposed. Most traditional near-field source localization algorithms suffer from excessive computational complexity or incompatibility with existing array architectures. To address such issues, this letter proposes a novel near-field sensing algorithm that combines coarse and fine granularity of spectrum peak search. Specifically, a spectral pattern in the angle domain is first constructed using FFT to identify potential angles where sources are present. Afterwards, a 1D beamforming is performed in the distance domain to obtain potential distance regions. Finally, a refined 2D multiple signal classification (MUSIC) is conducted within each narrowed angle-distance region to estimate the precise location of the sources. Numerical results demonstrate that the proposed algorithm can significantly reduce the computational complexity of 2D spectrum peak searches and achieve target localization with high-resolution.