Robust Information Filtering Techniques for Static and Dynamic State (State Estimation)

This project aims to improve upon the recent advances in state estimation techniques for nonlinear dynamical systems. These include the unscented Kalman filter, the particle filter, and the sigma-point filters. Existing methods rely heavily on the accurate model knowledge for the specific state estimation domain, whereas many problems involve uncertainty about the dynamic equations and the noise distributions. Our purpose is to develop robust state estimation algorithms based on information theoretic estimation principles that can handle such uncertainties as well as outliers and sensor failures. Specifically, we are interested in the application of these techniques for unobtrusive monitoring of elderly in their homes using motion sensors and RFID transmitters in cooperation with the Point-of-Care Laboratory.

Funded by

NSF