State estimation

We are interested in both fundamental aspects related to model-based state estimation and applications. Fundamental issues of interest include the propagation of uncertainty for 6 degrees-of-freedom rigid bodies, and the use of Kalman filtering for such systems.

We apply these ideas to localization using radio systems, and are interested in optimal estimation with low-cost sensors, in challenging environments, and where the system’s inherent instability means that we are reliant on accurate and reliable estimation.

Some relevant results include: