DynHD: Hyperdimensional Computing Approach for Efficient Radar Spectrum Classification

Abstract

Radar technology plays a critical role in target detection, classification, and tracking. However, the computational demands of training deep neural networks (DNNs) on radar signals can be overwhelming, posing challenges for edge devices with limited energy and computing resources. In this article, we propose leveraging hyperdimensional computing (HDC), a brain-inspired computing paradigm, as an efficient alternative. HDC utilizes high-dimensional vectors for information representation and processing, offering robustness and energy efficiency. We propose a novel HDC classification algorithm named DynHD, with a dynamic HDC encoder that adapts to more challenging radar spectrum recognition tasks. We designed this mechanism to provide great flexibility to the HDC encoder that is otherwise fixed. Our evaluations demonstrate that HDC-based approaches achieve comparable accuracy to DNN-based methods with lower-computational complexity, making them suitable for resource-constrained devices. We achieve significant improvements in latency during training and inference phases, enabling efficient processing of radar signals on edge devices.