This project addresses the well-established challenge of “state estimation,” of estimating the current state of variables in a complex system which are not observed directly. Previous work in systems theory has developed well-perfected methods for systems whose variables are all continuous, or all discrete, under conditions where the dynamics of the system itself are already perfectly known. This team proposes a fundamental advance, by unifying recent breakthroughs addressing the challenge of what to do when the dynamical system is not perfectly known, in the general nonlinear case. The recent work to be drawn upon involves information-theoretic learning, sigma-point filtering, particle filtering concepts, and the use of recurrent neural networks and backpropagation through time (which offer major advantages as universal approximators of nonlinear dynamical systems).
Broader Benefits: Better state estimation will be important to all kinds of challenges in managing complex systems more effectively, whether by neural networks or other components. The testbed to be used here- the localization of elderly patients in a prototype advanced health care clinic – was chosen both for its value as a challenge to the basic research and for its promise as a starting point for large real-world benefits.
Northeastern University’s College of Engineering is home to numerous federally-funded research centers and an array of leading-edge projects and initiatives that advance discovery and new knowledge in health, sustainability, and security.