Evaluation of Spiking Neural Network (SNN) Models for Detection & Classification Using FMCW Radar Data

Abstract

Processing radar data presents significant challenges due to its high dimensionality, noise, and temporal complexity, which make extracting robust and efficient features difficult. Spiking neural networks (SNNs) offer a biologically inspired alternative to conventional artificial neural networks (ANNs), with the promise of energy efficiency and temporal information processing. This work aims to evaluate the feasibility of SNN models for classification and detection tasks on frequency-modulated continuous-wave (FMCW) radar data, particularly in the industrial context of BEA. To address this, a complete dataset of FMCW radar signals was created and automatically annotated using a dedicated tool developed for this project. The dataset was then analyzed to characterize the signals and their variability. Several SNN models were trained and evaluated on the tasks, alongside standard ANN baselines for comparison. The experimental protocol included various neural encoding strategies, rigorous training, and consistent evaluation to ensure fair benchmarking. Performance was assessed in terms of classification accuracy, energy consumption (both theoretical and practical), and suitability for embedded deployment. Results demonstrate that while ANNs slightly outperform SNNs in accuracy, SNNs offer substantial gains in energy efficiency, making them highly suitable for low-power applications. Furthermore, deployment tests confirm that SNNs can be effectively implemented on classical embedded hardware, offering a promising pathway for low-power radar based sensing. This thesis contributes : i An automatic annotation tool for FMCW radar data. ii A ready-to-use FMCW dataset for BEA applications. iii An empirical and conceptual understanding of SNN behavior on radar data. iv A detailed evaluation of SNN viability in terms of accuracy and energy consumption. v A proof-of-concept embedded implementation that demonstrates integration into existing BEA products.