For the first time identification of human individuals using micro-Doppler (m-D) features measured at X-band has been demonstrated. Deep Convolutional Neural Networks (DCNNs) have been used to perform classification. Inspection and visualization of the classification results were performed using Uniform Manifold Approximation and Projection (UMAP). Classification accuracy of above 93.5% is obtained for a population of 22 subjects. The results show that human identification on a specific population based on X-band m-D measurements can be performed reliably using a DCNN.
|Title of host publication||EuRAD 2020 - 2020 17th European Radar Conference|
|Publisher||Institute of Electrical and Electronics Engineers (IEEE)|
|Number of pages||4|
|Publication status||Published - 2021|
|Event||17th European Radar Conference, EuRAD 2020 - Utrecht, Netherlands|
Duration: 13 Jan 2021 → 15 Jan 2021
|Name||EuRAD 2020 - 2020 17th European Radar Conference|
|Conference||17th European Radar Conference, EuRAD 2020|
|Period||13/01/21 → 15/01/21|
Bibliographical noteGreen Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.
- deep learning