用高维计算提升低信噪比下的波达方向估计精度与能效。
HYPERDOA: Robust and Efficient DoA Estimation using Hyperdimensional Computing
- 将波达方向估计转化为模式识别，利用高维计算抗噪性强的特性。
- 在低信噪比相干源场景下，准确率比现有方法高约35.39%。
- 能在嵌入式平台实现比神经网络少93%的能耗，适合边缘安全应用。
波达方向（DoA）估计面临显著权衡：传统方法在低信噪比（SNR）条件下精度不足，而现代深度学习方法则因能耗过高且缺乏可解释性，难以应用于资源受限的安全关键系统。本文提出HYPERDOA，一种基于高维计算（HDC）的新颖估计算法。该框架引入两种特征提取策略——均值空间-时延自相关与空间平滑——构建其HDC流水线，并将DoA估计重构为模式识别问题。该方法利用HDC固有的抗噪能力及透明代数运算，避免了传统方法中昂贵的矩阵分解和深度学习方法的“黑箱”特性。实验表明，在低信噪比、相干源场景下，HYPERDOA相比最先进方法准确率提升约35.39%；在嵌入式NVIDIA Jetson Xavier NX平台上，能耗较同类神经基线降低约93%。这一精度与能效的双重优势，使HYPERDOA成为边缘设备上任务关键型应用的可靠解决方案。
原文摘要 · Abstract (English)
Direction of Arrival (DoA) estimation techniques face a critical trade-off, as classical methods often lack accuracy in challenging, low signal-to-noise ratio (SNR) conditions, while modern deep learning approaches are too energy-intensive and opaque for resource-constrained, safety-critical systems. We introduce HYPERDOA, a novel estimator leveraging Hyperdimensional Computing (HDC). The framework introduces two distinct feature extraction strategies -- Mean Spatial-Lag Autocorrelation and Spatial Smoothing -- for its HDC pipeline, and then reframes DoA estimation as a pattern recognition problem. This approach leverages HDC's inherent robustness to noise and its transparent algebraic operations to bypass the expensive matrix decompositions and "black-box" nature of classical and deep learning methods, respectively. Our evaluation demonstrates that HYPERDOA achieves ~35.39% higher accuracy than state-of-the-art methods in low-SNR, coherent-source scenarios. Crucially, it also consumes ~93% less energy than competing neural baselines on an embedded NVIDIA Jetson Xavier NX platform. This dual advantage in accuracy and efficiency establishes HYPERDOA as a robust and viable solution for mission-critical applications on edge devices.
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