HYPERDOA: Robust and Efficient DoA Estimation using Hyperdimensional Computing

• URL: http://arxiv.org/abs/2510.10718v1
• Date: Sun, 12 Oct 2025 17:42:01 GMT
• Title: HYPERDOA: Robust and Efficient DoA Estimation using Hyperdimensional Computing
• Authors: Rajat Bhattacharjya, Woohyeok Park, Arnab Sarkar, Hyunwoo Oh, Mohsen Imani, Nikil Dutt,
• Abstract summary: We introduce HYPERDOA, a novel estimator leveraging Hyperdimensional Computing (HDC)<n>It achieves 35.39% higher accuracy than state-of-the-art methods in low-SNR, coherent-source scenarios.<n>It also consumes 93% less energy than competing neural baselines on an embedded NVIDIA Jetson Xavier NX platform.
• Score: 8.27483835715597
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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