Bayesian Learning for Object Recognition from Noisy Time Series Data.

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This research aims to capitalize on recent advances in Bayesian modelling and supervised learning to introduce a novel framework for robust target recognition from time-series data, with particular focus on radar, e.g. unmanned air-traffic management applications or robotics. The proposed approach can not only treat noisy sensory observations with intermittent and potentially asynchronous salient features, but also effectively exploit underlying spatio-temporal dependencies to achieve improved sequential target classification results. Amongst the key tackled challenges is maintaining computational as well as training-data efficiency and interpretability of the develop technique. Real data from Aveillant's Gamekeeper radar is expected to be made available to evaluate and benchmark (e.g. versus convolutional neural networks or other standard machine learning classifiers) the performance of the introduced method(s).