Joint single-shot ToA and DoA estimation for VAA-based BLE ranging with phase ambiguity: A deep learning-based approach

TL;DR

This paper introduces a deep learning-based method for joint ToA and DoA estimation using virtual antenna arrays on BLE devices, overcoming phase ambiguity with a neural network approach, enabling accurate angle and distance measurements.

Contribution

It presents a novel neural network framework that resolves phase ambiguity in single-shot CFR, facilitating super resolution ToA and DoA estimation on size-constrained BLE devices.

Findings

Achieves mean square errors near the Cramer Rao bound at SNR ≥ 5 dB.

Effectively resolves phase ambiguity with a voting mechanism.

Demonstrates superior performance with non-uniform VAAs.

Abstract

Conventional direction-of-arrival (DoA) estimation methods rely on multi-antenna arrays, which are costly to implement on size-constrained Bluetooth Low Energy (BLE) devices. Virtual antenna array (VAA) techniques enable DoA estimation with a single antenna, making angle estimation feasible on such devices. However, BLE only provides a single-shot two-way channel frequency response (CFR) with a binary phase ambiguity issue, which hinders the direct application of VAA. To address this challenge, we propose a unified model that combines VAA with BLE two-way CFR, and introduce a neural network based phase recovery framework that employs row / column predictors with a voting mechanism to resolve the ambiguity. The recovered one-way CFR then enables super resolution algorithms such as MUSIC for joint time of arrival (ToA) and DoA estimation. Simulation results demonstrate that the proposed method achieves superior performance under non-uniform VAAs, with mean square errors approaching the Cramer Rao bound at SNR $\geq$ 5 dB.