2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information
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Paper Detail

Paper IDSPTM-14.2
Paper Title ADAPTIVE REAL-TIME FILTER FOR PARTIALLY-OBSERVED BOOLEAN DYNAMICAL SYSTEMS
Authors Mahdi Imani, George Washington University, United States; Seyede Fatemeh Ghoreishi, University of Maryland, United States
SessionSPTM-14: Models, Methods and Algorithms 2
LocationGather.Town
Session Time:Thursday, 10 June, 13:00 - 13:45
Presentation Time:Thursday, 10 June, 13:00 - 13:45
Presentation Poster
Topic Signal Processing Theory and Methods: [ASP] Adaptive Signal Processing
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Abstract Partially-Observed Boolean dynamical systems (POBDS) are a general class of nonlinear state-space models consisting of a hidden Boolean state process observed through an arbitrary noisy mapping to a measurement space. The huge uncertainty present in systems/processes, along with the time-limit constraints necessitate real-time or online joint state and parameter estimation of POBDS. In this manuscript, we present a real-time joint state and parameter estimation framework for POBDS. The proposed framework relies on complete-sufficient statistic of parameters, where joint state and parameter estimation is achieved based on the combination of online expectation-maximization method, and the optimal MMSE state estimator for POBDS, called Boolean Kalman filter. The performance of the proposed method is assessed through a POBDS model for Boolean gene regulatory networks observed through noisy measurements.