基于相位模糊的VAA-BLE测距中单次-shot ToA和DoA估计：一种深度学习方法
Joint single-shot ToA and DoA estimation for VAA-based BLE ranging with phase ambiguity: A deep learning-based approach
AI总结：
本文提出一种基于深度学习的VAA-BLE测距方法，通过解决相位模糊问题实现单次-shot ToA和DoA联合估计。
摘要：
传统方向到达（DoA）估计方法依赖于多天线阵列，这在尺寸受限的蓝牙低能耗（BLE）设备上实现成本较高。虚拟天线阵列（VAA）技术使单个天线能够实现DoA估计，使得在这些设备上进行角度估计成为可能。然而，BLE只能提供单次双向通道频率响应（CFR）并存在二进制相位模糊问题，这阻碍了VAA的直接应用。为了解决这一挑战，我们提出了一种结合VAA与BLE双向CFR的统一模型，并引入了一个基于神经网络的相位恢复框架，该框架使用行/列预测器和投票机制来解决模糊问题。恢复的一维CFR随后可以用于超分辨算法如MUSIC进行联合到达时间（ToA）和DoA估计。仿真结果表明，所提出的方法在非均匀VAA下实现了优越的性能，均方误差在SNR≥5 dB时接近CRB。
英文摘要：
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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