Joint single-shot ToA and DoA estimation for VAA-based BLE ranging with phase ambiguity: A deep learning-based approach
Jincheng Xie, Yili Deng, Jiguang He, Pengyu Wang, Miaomiao Dong, Rui Tang, Zhongyi Huang
TL;DR
This work tackles joint ToA and DoA estimation for BLE devices by fusing virtual antenna array (VAA) geometry with the two-way CFR construct to exploit single-shot measurements. A binary phase ambiguity arises from the square-root recovery $(\hat{\mathbf{Y}})^{1/2} = \mathbf{Y} \circ \mathbf{N}$ with $\mathbf{N} \in \{ \pm 1\}^{N \times M}$, which is resolved by a neural voting framework using row-wise and column-wise networks. After ambiguity recovery, the corrected one-way CFR is used with MUSIC to obtain high-resolution ToA and DoA estimates, achieving performance close to the Cramer–Rao lower bound at moderate SNR. The approach enables accurate localization on resource-constrained BLE hardware by avoiding multi-antenna arrays and heavy calibration.
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.