| 1 |
Noise covariance identification for nonlinear systems using expectation maximization and moving horizon estimation
Ge M, Kerrigan EC
Automatica, 77, 336, 2017 |
| 2 |
On Autocovariance Least-Squares Method for Noise Covariance Matrices Estimation
Dunik J, Straka O, Simandl M
IEEE Transactions on Automatic Control, 62(2), 967, 2017 |
| 3 |
Noise covariance identification for time-varying and nonlinear systems
Ge M, Kerrigan EC
International Journal of Control, 90(9), 1903, 2017 |
| 4 |
A New Family of High-Resolution Multivariate Spectral Estimators
Zorzi M
IEEE Transactions on Automatic Control, 59(4), 892, 2014 |
| 5 |
Fault detection approach for systems involving soft sensors
Serpas M, Chu YF, Hahn J
Journal of Loss Prevention in The Process Industries, 26(3), 443, 2013 |
| 6 |
Nonlinear Stochastic Modeling to Improve State Estimation in Process Monitoring and Control
Lima FV, Rawlings JB
AIChE Journal, 57(4), 996, 2011 |
| 7 |
Identification of process and measurement noise covariance for state and parameter estimation using extended Kalman filter
Bavdekar VA, Deshpande AP, Patwardhan SC
Journal of Process Control, 21(4), 585, 2011 |
| 8 |
Estimation of the disturbance structure from data using semidefinite programming and optimal weighting
Rajamani MR, Rawlings JB
Automatica, 45(1), 142, 2009 |
| 9 |
A generalized autocovariance least-squares method for Kalman filter tuning
Akesson BM, Jorgensen JB, Poulsen NK, Jorgensen SB
Journal of Process Control, 18(7-8), 769, 2008 |
| 10 |
A new autocovariance least-squares method for estimating noise covariances
Odelson BJ, Rajamani MR, Rawlings JB
Automatica, 42(2), 303, 2006 |
