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.

翻译：波达方向（DoA）估计技术面临一个关键的权衡：经典方法在具有挑战性的低信噪比（SNR）条件下往往精度不足，而现代深度学习方法对于资源受限、安全关键的系统而言又过于耗能且不透明。我们提出了HYPERDOA，一种利用超维计算（HDC）的新型估计器。该框架为其HDC流水线引入了两种不同的特征提取策略——平均空间滞后自相关和空间平滑，然后将DoA估计重新构建为一个模式识别问题。该方法利用HDC固有的噪声鲁棒性及其透明的代数运算，分别绕过了经典方法中昂贵的矩阵分解和深度学习方法的“黑箱”性质。我们的评估表明，在低信噪比、相干源场景下，HYPERDOA比最先进的方法实现了约35.39%的更高精度。至关重要的是，在嵌入式NVIDIA Jetson Xavier NX平台上，其能耗也比竞争的神经基线方法降低了约93%。这种在精度和效率上的双重优势，确立了HYPERDOA作为边缘设备上任务关键型应用的一个鲁棒且可行的解决方案。