May 18 (Wed) @ 3:30pm: "State Estimation of Sampled-Data Systems including Applications to Vehicle Navigation and Tracking," Sharad Shankar, ECE PhD Defense

Abstract

State estimation is a crucial part of navigation and control methods, however, most well-known state estimation techniques assume some combination of linearity, Gaussian noise, as well as uniform sampling and synchrony of the system process and measurement sources. This is a limitation for problems like integrated aircraft navigation, switched circuit monitoring, and maneuvering vehicle tracking where these simplifying assumptions may not hold. In this presentation, state estimation methods that accommodate these facets of real-world problems are explored. Methods discussed include finite-horizon nonlinear real-time optimization methods and interacting multiple-model Kalman filters for asynchronous switching systems. Some theoretical convergence results are presented along with results in simulations based on the aforementioned real-world systems.

Bio

Sharad Shankar has been a PhD student in the Electrical and Computer Engineering department at UCSB since 2016. Before that, he received a B.S. in Electrical Engineering from UCLA. Sharad's research interests include nonlinear and hybrid state estimation along with stochastic planning and control. His hobbies include hiking, music, and mechanical tinkering.

Hosted by: Professor João Hespanha

Submitted by: Sharad Shankar <sharad@ucsb.edu>