Fioranelli, F;
Ritchie, MA;
Gurbuz, S;
Griffiths, H;
(2017)
Feature diversity for optimized human micro-Doppler classification using multistatic radar.
IEEE Transactions on Aerospace and Electronic Systems
, 53
(2)
pp. 640-654.
10.1109/TAES.2017.2651678.
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Abstract
This paper investigates the selection of different combinations of features at different multistatic radar nodes, depending on scenario parameters, such as aspect angle to the target and signal-to-noise ratio, and radar parameters, such as dwell time, polarisation, and frequency band. Two sets of experimental data collected with the multistatic radar system NetRAD are analysed for two separate problems, namely the classification of unarmed vs potentially armed multiple personnel, and the personnel recognition of individuals based on walking gait. The results show that the overall classification accuracy can be significantly improved by taking into account feature diversity at each radar node depending on the environmental parameters and target behaviour, in comparison with the conventional approach of selecting the same features for all nodes.
| Type: | Article |
|---|---|
| Title: | Feature diversity for optimized human micro-Doppler classification using multistatic radar |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/TAES.2017.2651678 |
| Publisher version: | http://doi.org/10.1109/TAES.2017.2651678 |
| Language: | English |
| Additional information: | © 2017 IEEE. This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Multistatic radar, human micro-Doppler, feature extraction, feature selection, classification, radar signatures. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Electronic and Electrical Eng |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/1522382 |
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