Low-Angle Target Tracking in Sea Surface Multipath Using Convolutional Neural Networks

• 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)