Boolean Kalman Filter with Correlated Observation Noise Conference Paper

abstract

• 2017 IEEE. This paper is concerned with optimal estimation of the state of a Boolean dynamical systems observed through correlated noisy Boolean measurements. The optimal Minimum Mean-Square Error (MMSE) state estimator for general Partially-Observed Boolean Dynamical Systems (POBDS) can be computed via the Boolean Kalman Filter (BKF). However, thus far in the literature only the case of white observation noise has been considered. In this paper, we develop the optimal MMSE filter for a class of POBDS with correlated Boolean measurements. The performance of the proposed method is subsequently investigated using the p53-MDM2 negative feedback loop genetic network model.

name of conference

• 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

authors

• Braga Neto, Ulisses

published proceedings

• 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

author list (cited authors)

• McClenny, L. D., Imani, .., & Braga-Neto, U. M.

citation count

• 15

complete list of authors

• McClenny, Levi D||Braga-Neto, Ulisses M

publication date

• January 2017

publisher

• Institute of Electrical and Electronics Engineers (IEEE)  Publisher

published in

• Proceedings of the ... IEEE International Conference on Acoustics, Speech, and Signal Processing / sponsored by the Institute of Electrical and Electronics Engineers Signal Processing Society. ICASSP (Conference)  Journal

Digital Object Identifier (DOI)

• 10.1109/icassp.2017.7952279

International Standard Book Number (ISBN) 13

• 9781509041176

start page

• 866

end page

• 870

URL

• http://dx.doi.org/10.1109/icassp.2017.7952279