用深度学习解决蓝牙定位中单天线角度估计的相位模糊问题
Joint single-shot ToA and DoA estimation for VAA-based BLE ranging with phase ambiguity: A deep learning-based approach
- 构建融合虚拟阵列与双向信道响应的统一模型
- 通过投票机制神经网络恢复相位，使均方误差逼近克拉美罗界
- 适合资源受限设备上的高精度定位应用
传统到达方向（DoA）估计依赖多天线阵列，难以在尺寸受限的蓝牙低能耗（BLE）设备上实现。虚拟天线阵列（VAA）技术可在单天线条件下实现角度估计，但BLE仅提供单次双向信道频率响应（CFR），存在二值相位模糊问题，阻碍了VAA的直接应用。为此，本文提出一种统一模型，结合VAA与BLE双向CFR，引入基于神经网络的相位恢复框架，采用行/列预测器与投票机制解决相位模糊。恢复的一维CFR使超分辨算法（如MUSIC）可实现联合到达时间（ToA）与到达方向（DoA）估计。仿真结果表明，该方法在非均匀VAA下性能优越，在信噪比≥5 dB时，均方误差逼近克拉美罗界。
原文摘要 · Abstract (English)
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.
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