Full Motion State Localization with Extra Large Aperture Arrays

TL;DR

This paper explores full motion state localization using large aperture arrays in near-field conditions, developing models, bounds, and estimation methods to improve accuracy in complex propagation environments.

Contribution

It introduces a comprehensive NF signal model, analyzes information bounds, and proposes an ML estimator for 8D localization with ELAA in near-field scenarios.

Findings

Delay measurements provide richer information than Doppler.

Doppler alone cannot fully estimate position due to channel uncertainties.

The proposed ML approach jointly estimates position, velocity, and orientation.

Abstract

Conventional localization techniques typically assume far-field (FF) propagation characterized by planar wavefronts and simplified spatial relationships. The use of higher carrier frequencies has given rise to the paradigm of extra large aperture arrays (ELAAs) which consist of a large number of tightly packed antenna elements. These arrays have a large electrical aperture which increases the Fraunhofer distance making the FF assumption restrictive. As a result, near-field (NF) effects, such as spherical wavefront curvature, direction dependent gains, and spatial variations in Doppler and delay, become significant even at distances previously regarded as FF. This paradigm shift opens up new opportunities: the rich multi-parametric structure of NF models if properly exploited can enable superior localization accuracy. In this work, we investigate the potential of multi-snapshot, full-motion state (3D position, 3D velocity, and 2D orientation) estimation using delay and Doppler measurements for a mobile receiver equipped with a linear ELAA in an environment comprising a number of wideband anchors. We develop a signal model that captures both the NF propagation geometry and spatially varying Doppler effects. We perform an information-theoretic analysis to establish Cramer-Rao lower bounds (CRLB) on the achievable position error bound (PEB), velocity error bound (VEB), and orientation error bound (OEB), respectively. We reveal that delay measurements carry richer information than Doppler measurements, and standalone Doppler measurements cannot overcome information losses due to unknown channel gains and frequency offsets, enabling only coarse estimation capabilities. We also propose a maximum-likelihood (ML) approach to jointly estimate the 8D position parameters from measured channel characteristics.