Aladdin

Demonstration of the use of novel approaches to actively select when delayed measurements will contribute toward estimation quality and should be acquired and fused.

We are developing efficient algorithms to perform online selection of variables from streaming data. Such algorithms have application in the problem of selecting readings from a subset of sensors to ensure the best possible prediction of future measurements, whilst minimizing bandwidth consumption. A demonstration of a novel algorithm (that extends the Lasso algorithm to incorporate reinforcement learning) is shown here, where it is used to select in real time, which subset of air temperature sensors to request a reading from, in order to achieve the best possible prediction.

We are developing robust fusion algorithms based on generalized covariance union (GCU) that allows consistent multiple hypothesis estimation in the face of faulty or untrustworthy sensors, or incomplete probabilistic models. A demonstration of this algorithm applied to the problem of inferring the temperature of surrounding buildings from a network of sensors (some of which are faulty) deployed within a city (based on the Robocup Rescue Simulation Environment) is shown in the demo.

We are developing computationally efficient online formulations of multiple output Gaussian process and applying them to sensor network data. The Gaussian process learns delays and correlations between sensors to constructs a probabilistic model that can be used to predict missing sensor values, make short term predictions into the future, or perform adaptive sampling (taking the minimum number to ensure that uncertainty is maintained below a threshold value).