Vishwakarma, S;
Li, W;
Adve, R;
Chetty, K;
(2023)
Learning Salient Features in Radar Micro-Doppler Signatures Using Attention Enhanced Alexnet.
In:
International Conference on Radar Systems (RADAR 2022).
(pp. pp. 190-195).
IET
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Abstract
This work introduces an attention mechanism that can be integrated into any standard convolution neural network (CNN) to improve model sensitivity and prediction accuracy with minimal computational overhead. We introduce the attention mechanism in a lightweight network - Alexnet and evaluate its classification performance for human micro-Doppler signatures. We show that the Alexnet model trained with an attention module can implicitly learn to highlight the salient regions in the radar signatures whilst suppressing the irrelevant background regions and consistently improve the network predictions by more than 4% in most cases. We further provide network visualizations through class activation mapping, providing better insights into how the predictions are made.
| Type: | Proceedings paper |
|---|---|
| Title: | Learning Salient Features in Radar Micro-Doppler Signatures Using Attention Enhanced Alexnet |
| Event: | International Conference on Radar Systems (RADAR 2022) |
| ISBN-13: | 978-1-83953-777-6 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1049/icp.2022.2314 |
| Publisher version: | https://doi.org/10.1049/icp.2022.2314 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions. |
| Keywords: | Radar Sensing, Attention Networks, Deep Learning, Micro-Doppler Signatures, Human Activity Recognition |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Security and Crime Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10182857 |
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