BIMSA > Seminar on Control Theory and Nonlinear Filtering Optimal Estimation with Missing Observations via Balanced Time-Symmetric Stochastic Models
Optimal Estimation with Missing Observations via Balanced Time-Symmetric Stochastic Models
Organizer
Shing Toung Yau
Speaker
Jia Yi Kang
Time
Thursday, November 3, 2022 8:30 PM - 9:00 PM
Venue
Online
Abstract
I will introduce a work considered data fusion for the purpose of smoothing and interpolation based on observation records with missing data. Stochastic processes are generated by linear stochastic models. The paper begins by drawing a connection between time reversal in stochastic systems and all-pass extensions. A particular normalization (choice of basis) between the two time-directions allows the two to share the same orthonormalized state process and simplifies the mathematics of data fusion. In this framework, they derive symmetric and balanced Mayne–Fraser-like formulas that apply simultaneously to continuous-time smoothing and interpolation, providing a definitive unification of these concepts. The absence of data over subintervals requires in general a hybrid filtering approach involving both continuous-time and discrete-time filtering steps.