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Low-Angle Target Tr...
Low-Angle Target Tracking in Sea Surface Multipath Using Convolutional Neural Networks
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- Karlsson, Alexander (författare)
- KTH,Teknisk informationsvetenskap,SAAB AB, Business Area Surveillance, Product Unit Electronic Surveillance, Stockholm, 11428, Sweden
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- Jansson, Magnus, Professor (författare)
- KTH,Teknisk informationsvetenskap
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- Hämäläinen, Mikael (författare)
- SAAB AB, Business Area Surveillance, Product Unit Electronic Surveillance, Stockholm, 11428, Sweden
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(creator_code:org_t)
- New York, USA : Institute of Electrical and Electronics Engineers (IEEE), 2023
- 2023
- Engelska.
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Ingår i: IEEE Transactions on Aerospace and Electronic Systems. - New York, USA : Institute of Electrical and Electronics Engineers (IEEE). - 0018-9251 .- 1557-9603. ; 59:5, s. 6813-6831
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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
- Multipath interference while tracking sea-skimming targets can significantly distort the estimated height of the target. If accounted for however, this interference can be used to obtain more accurate estimates. In this study, we accomplish this with a convolutional neural network (CNN) used as a parameter estimator. The performance of this network is compared with maximum likelihood and least-squares methods. We found that the CNN performs well in comparison to these methods with only a fraction of the computations.
Ämnesord
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering (hsv//eng)
Nyckelord
- deep learning
- frequency agile radar
- low angle tracking
- multipath
- parameter estimation
- phase monopulse
- Electrical Engineering
- Elektro- och systemteknik
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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