Bayesian Learning for Object Recognition from Noisy Time Series Data.

这项研究旨在利用贝叶斯建模和监督学习的最新进展，从时间序列数据中引入一个新的鲁棒目标识别框架，特别关注雷达，例如无人驾驶空中交通管理应用或机器人。所提出的方法不仅可以处理嘈杂的感官观察间歇性和潜在的异步显着特征，但也有效地利用潜在的时空依赖性，以实现改进的顺序目标分类结果。解决的关键挑战之一是保持计算以及训练数据的效率和开发技术的可解释性。来自Aveillant的Gamekeeper雷达的真实的数据预计将用于评估和基准测试（例如，与卷积神经网络或其他标准机器学习分类器相比）所引入的方法的性能。

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).