HYPERDOA: Robust and Efficient DoA Estimation using Hyperdimensional Computing
This provides a robust and efficient solution for mission-critical applications on resource-constrained edge devices, addressing a domain-specific bottleneck.
The paper tackles the trade-off between accuracy and energy efficiency in Direction of Arrival (DoA) estimation by introducing HYPERDOA, a method using Hyperdimensional Computing, which achieves ~35.39% higher accuracy in low-SNR scenarios and consumes ~93% less energy than neural baselines on embedded platforms.
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.