{"ID":2856854,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.10718","arxiv_id":"2510.10718","title":"HYPERDOA: Robust and Efficient DoA Estimation using Hyperdimensional Computing","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.","short_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 HYPER...","url_abs":"https://arxiv.org/abs/2510.10718","url_pdf":"https://arxiv.org/pdf/2510.10718v2","authors":"[\"Rajat Bhattacharjya\",\"Woohyeok Park\",\"Arnab Sarkar\",\"Hyunwoo Oh\",\"Mohsen Imani\",\"Nikil Dutt\"]","published":"2025-10-12T17:42:01Z","proceeding":"eess.SP","tasks":"[\"eess.SP\",\"cs.AI\",\"cs.AR\",\"cs.SC\"]","methods":"[]","has_code":false}