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Model-Aided Drone C...
Model-Aided Drone Classification Using Convolutional Neural Networks
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- Karlsson, Alexander (författare)
- KTH,Teknisk informationsvetenskap,Product Unit Electronic Surveillance, Business Area Surveillance, SAAB AB, Stockholm, Sweden
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- Jansson, Magnus, Professor (författare)
- KTH,Teknisk informationsvetenskap
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- Hämäläinen, Mikael (författare)
- Product Unit Electronic Surveillance, Business Area Surveillance, SAAB AB, Stockholm, Sweden
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(creator_code:org_t)
- Institute of Electrical and Electronics Engineers (IEEE), 2022
- 2022
- Engelska.
- Relaterad länk:
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https://kth.diva-por... (primary) (Raw object)
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visa fler...
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- Classifiers using convolutional neural networks (CNNs) often yield high accuracies on samples that come from the same distribution as the training data. In this study we evaluate a CNN classifier's ability to discriminate drones from non-drone targets, such as birds, when they are not represented in the training data. We found that the mean accuracy on such out-of-distribution drones was 78%. By introducing a synthetic drone class, generated from a mathematical model, the out-of-distribution drone accuracy was improved to 86%. When trained on all drone types the mean accuracy over all classes was 90%. The data was collected with a 77 GHz mechanically scanning radar with only 9 ms dwell time.
Ämnesord
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik -- Signalbehandling (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering -- Signal Processing (hsv//eng)
Nyckelord
- classification
- FMCW radar
- deep learning
- drone
- UAV
- bird
- RCS
- millimeter wave
Publikations- och innehållstyp
- ref (ämneskategori)
- kon (ämneskategori)