Learning salient features in radar micro-Doppler signatures using Attention Enhanced Alexnet

Shelly Vishwakarma, Wenda Li, Raviraj Adve, Kevin Chetty

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)


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.
Original languageEnglish
Title of host publicationInternational Conference on Radar Systems (RADAR 2022)
Place of PublicationEdinburgh
PublisherInstitution of Engineering and Technology
Number of pages6
ISBN (Print)978-1-83953-777-6
Publication statusPublished - 24 Oct 2022
EventInternational Conference on Radar Systems 2022 - Murrayfield Stadium, Edinburgh, United Kingdom
Duration: 24 Oct 202227 Oct 2022


ConferenceInternational Conference on Radar Systems 2022
Abbreviated titleRADAR 2022
Country/TerritoryUnited Kingdom
Internet address


  • Radar Sensing
  • Attention Networks
  • Deep Learning
  • Micro-Doppler Signatures
  • Human Activity Recognition


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