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Title: On the Impact of Different Kernels and Training Data on a Gaussian Process Approach for Target Tracking
The application of multiple target tracking algorithms has exponentially increased during the last two decades. Recently, model-free approaches, such as Gaussian process regression and convolutional neural networks, have been developed for target tracking. This paper presents a simulation-based study on the practical aspects of a very promising and recently proposed Gaussian process method, namely the Gaussian process motion tracker [1]. The paper also provides design guidelines on the various aspects of the above-mentioned tracking method.  more » « less
Award ID(s):
1903466
PAR ID:
10161399
Author(s) / Creator(s):
;
Date Published:
Journal Name:
the International Conference on Information Fusion (Fusion 2020)
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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