Domain adaptation for target classification using micro-Doppler spectra in radar networks

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

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 languageEnglish
Title of host publication2021 IEEE 24th International Conference on Information Fusion (FUSION)
Subtitle of host publicationProceedings
PublisherIEEE
Pages1-8
Number of pages8
ISBN (Electronic)978-1-7377497-1-4
ISBN (Print)978-1-6654-1427-2
Publication statusPublished - 2021
Event2021 IEEE 24th International Conference on Information Fusion (FUSION) - Hybrid at Sun City, South Africa
Duration: 1 Nov 20214 Nov 2021
Conference number: 24th

Conference

Conference2021 IEEE 24th International Conference on Information Fusion (FUSION)
Abbreviated titleFusion 2021
CountrySouth Africa
CityHybrid at Sun City
Period1/11/214/11/21

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-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.

Keywords

  • radar target classification
  • micro-Doppler signature
  • domain adaptation
  • multi-static radar network

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