Abstract
In this paper, the classification of human activity from micro-Doppler spectrograms measured by a radar network is considered. To cope with differences between the training and test datasets due to changes in the set of participants, signal-to-noise ratio and polarimetry, domain adaptation is proposed. To realize this, linear mapping between the two domains is assumed and estimated by one of two methods, expectation-maximization or empirical estimates of statistical moments. The performance of the methods is evaluated on experimental data measured by a multi-static radar network. The proposed methods increase the classification accuracy by 5–15 percentiles on the recorded dataset.
| Original language | English |
|---|---|
| Title of host publication | 2021 IEEE 24th International Conference on Information Fusion (FUSION) |
| Subtitle of host publication | Proceedings |
| Publisher | IEEE |
| Pages | 1-8 |
| Number of pages | 8 |
| ISBN (Electronic) | 978-1-7377497-1-4 |
| ISBN (Print) | 978-1-6654-1427-2 |
| Publication status | Published - 2021 |
| Event | 2021 IEEE 24th International Conference on Information Fusion (FUSION) - Hybrid at Sun City, South Africa Duration: 1 Nov 2021 → 4 Nov 2021 Conference number: 24th |
Conference
| Conference | 2021 IEEE 24th International Conference on Information Fusion (FUSION) |
|---|---|
| Abbreviated title | Fusion 2021 |
| Country/Territory | South Africa |
| City | Hybrid at Sun City |
| Period | 1/11/21 → 4/11/21 |
Bibliographical note
Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-careOtherwise 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.
Keywords
- radar target classification
- micro-Doppler signature
- domain adaptation
- multi-static radar network