Attention‐enhanced Alexnet for improved radar micro‐Doppler signature classification

Shelly Vishwakarma, Wenda Li, Chong Tang, Karl Woodbridge, Ravi Raj Adve, Kevin Chetty

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)
45 Downloads (Pure)


This work introduces an attention mechanism that can be integrated into any standard convolution neural network to improve model sensitivity and prediction accuracy with minimal computational overhead. The attention mechanism is introduced in a lightweight network – Alexnet and its classification performance for human micro-Doppler signatures is evaluated. The Alexnet model trained with an attention module can implicitly highlight the salient regions in the radar signatures while suppressing the irrelevant background regions and consistently improving network predictions. Network visualizations are provided through class activation mapping, providing better insights into how the predictions are made. The visualizations demonstrate how the attention mechanism focusses on the region of interest in the radar signatures.
Original languageEnglish
Pages (from-to)1-13
Number of pages13
JournalIET Radar, Sonar & Navigation
Early online date30 Dec 2022
Publication statusE-pub ahead of print - 30 Dec 2022


  • image classification
  • micro Doppler
  • radar target recognition


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