Near-Field Position and Orientation Tracking With Hybrid ELAA Architecture

TL;DR

This paper introduces a novel hybrid ELAA architecture and a predictive analog combining method for accurate near-field position and orientation tracking of mobile stations, effectively reducing hardware complexity and improving performance.

Contribution

It proposes a predictive analog combining-assisted EKF framework that leverages temporal correlation for enhanced pose tracking with limited RF chains.

Findings

Significant improvement in tracking accuracy with fewer RF chains.

Effective analog combiner design under hardware constraints.

Quantitative analysis of factors affecting pose information extraction.

Abstract

This paper investigates near-field (NF) position and orientation tracking of a multi-antenna mobile station (MS) using an extremely large antenna array (ELAA)-equipped base station (BS) with a limited number of radio frequency (RF) chains. Under this hybrid array architecture, the received uplink pilot signal at the BS is first combined by analog phase shifters, producing a low-dimensional observation before digital processing. Such analog compression provides only partial access to the ELAA measurement, making it essential to design an analog combiner that can preserve pose-relevant signal components despite channel uncertainty and unit-modulus hardware constraints. To address this, we propose a predictive analog combining-assisted extended Kalman filter (PAC-EKF) framework, where the analog combiner can leverage the temporal correlation in the MS pose variation to capture the most informative signal components predictively. We then analyze fundamental performance limits via Bayesian Cram\'er-Rao bound and Fisher information matrix, explicitly quantifying how the analog combiner, array size, signal-to-noise ratio, and MS pose influence the pose information contained in the uplink observation. Building on these insights, we develop two methods for designing a low-complexity analog combiner. Numerical results show that the proposed predictive analog combining approach significantly improves tracking accuracy, even with fewer RF chains and lower transmit power.