Joint Motion, Angle, and Range Estimation in Near-Field under Array Calibration Imperfections

TL;DR

This paper introduces a low-complexity method for joint estimation of motion, angle, and range in near-field MIMO systems, leveraging spectral characteristics to improve accuracy and reduce computational demands.

Contribution

It proposes a novel spectral-based approach for joint parameter estimation in near-field MIMO, combining 2D-DFT and MUSIC for efficient and accurate results.

Findings

Achieves NMSE of -40 dB in location and velocity estimates

Reduces computational complexity compared to maximum likelihood methods

Provides accurate coarse estimates that are refined effectively

Abstract

Ultra-massive multiple-input multiple-output MIMO (UM-MIMO) leverages large antenna arrays at high frequencies, transitioning communication paradigm into the radiative near-field (NF), where spherical wavefronts enable full-vector estimation of both target location and velocity. However, location and motion parameters become inherently coupled in this regime, making their joint estimation computationally demanding. To overcome this, we propose a novel approach that projects the received two-dimensional space-time signal onto the angle-Doppler domain using a two-dimensional discrete Fourier transform (2D-DFT). Our analysis reveals that the resulting angular spread is centered at the target's true angle, with its width determined by the target's range. Similarly, transverse motion induces a Doppler spread centered at the true radial velocity, with the width of Doppler spread proportional to the transverse velocity. Exploiting these spectral characteristics, we develop a low-complexity algorithm that provides coarse estimates of angle, range, and velocity, which are subsequently refined using one-dimensional multiple signal classification (MUSIC) applied independently to each parameter. The proposed method enables accurate and efficient estimation of NF target motion parameters. Simulation results demonstrate a normalized mean squared error (NMSE) of -40 dB for location and velocity estimates compared to maximum likelihood estimation, while significantly reducing computational complexity.