| Paper ID | SPTM-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 |
| Session | SPTM-14: Models, Methods and Algorithms 2 |
| Location | Gather.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 |
| IEEE Xplore Open Preview |
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| Virtual Presentation |
Click here to watch in the Virtual Conference |
| 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. |
