A Gaussian Process Approach for Extended Object Tracking with Random Shapes and for Dealing with Intractable Likelihoods

Aftab, W., De Freitas, A., Arvaneh, M. et al. (1 more author) (2017) A Gaussian Process Approach for Extended Object Tracking with Random Shapes and for Dealing with Intractable Likelihoods. In: Proceedings of the 2017 International Conference on Digital Signal Processing (DSP). 2017 22nd International Conference on on Digital Signal Processing (DSP 2017), 23 - 25 August 2017, London, UK. IEEE .

Abstract

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Authors/Creators:
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Keywords: Gaussian process approach; random shapes; intractable likelihoods; arbitrarily shaped extended objects; object associations; sensor noise; extended object tracking problems; axis-symmetric properties; measurement noise; convolution particle filter
Dates:
  • Published: 7 November 2017
  • Accepted: 25 June 2017
Institution: The University of Sheffield
Academic Units: The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Automatic Control and Systems Engineering (Sheffield)
Funding Information:
FunderGrant number
EUROPEAN COMMISSION - FP6/FP7TRAX - 607400
Depositing User: Symplectic Sheffield
Date Deposited: 14 Jul 2017 08:43
Last Modified: 25 Jan 2018 12:51
Published Version: https://doi.org/10.1109/ICDSP.2017.8096087
Status: Published
Publisher: IEEE
Refereed: Yes
Identification Number: 10.1109/ICDSP.2017.8096087

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