Robust Vehicle Positioning based on Multi-Epoch and Multi-Antenna TOAs in Harsh Environments

TL;DR

This paper introduces a robust multi-epoch, multi-antenna TOA positioning method for harsh environments, utilizing SDP-based initialization and iterative refinement to improve accuracy and avoid ambiguities.

Contribution

It proposes a novel SDP-based initialization (MEMA-SDP) and an iterative refinement for multi-epoch, multi-antenna TOA localization, enhancing robustness and accuracy in challenging conditions.

Findings

MEMA-SDP provides accurate initial estimates close to true locations.

The iterative refinement guarantees near-global optimality.

The method outperforms conventional single-epoch approaches in simulations.

Abstract

For radio-based time-of-arrival (TOA) positioning systems applied in harsh environments, obstacles in the surroundings and on the vehicle itself will block the signals from the anchors, reduce the number of available TOA measurements and thus degrade the localization performance. Conventional multi-antenna positioning technique requires a good initialization to avoid local minima, and suffers from location ambiguity due to insufficient number of TOA measurements and/or poor geometry of anchors at a single epoch. A new initialization method based on semidefinite programming (SDP), namely MEMA-SDP, is first designed to address the initialization problem of the MEMA-TOA method. Then, an iterative refinement step is developed to obtain the optimal positioning result based on the MEMA-SDP initialization. We derive the Cramer-Rao lower bound (CRLB) to analyze the accuracy of the new MEMA-TOA method theoretically, and show its superior positioning performance over the conventional single-epoch and multi-antenna (SEMA) localization method. Simulation results in harsh environments demonstrate that i) the new MEMA-SDP provides an initial estimation that is close to the real location, and empirically guarantees the global optimality of the final refined positioning solution, and ii) compared with the conventional SEMA method, the new MEMA-TOA method has higher positioning accuracy without location ambiguity, consistent with the theoretical analysis.