FIR filtering of state-space models in non-Gaussian environment with uncertainties
TELE-INFO'11/MINO'11/SIP'11 Proceedings of the 10th WSEAS international conference on Telecommunications and informatics and microelectronics, nanoelectronics, optoelectronics, and WSEAS international conference on Signal processing
Effect of first- and second-order extensions on UFIR filtering of nonlinear models
AMERICAN-MATH'12/CEA'12 Proceedings of the 6th WSEAS international conference on Computer Engineering and Applications, and Proceedings of the 2012 American conference on Applied Mathematics
Efficient denoising of piecewise-smooth signals with forward-backward FIR smoothers
AMERICAN-MATH'12/CEA'12 Proceedings of the 6th WSEAS international conference on Computer Engineering and Applications, and Proceedings of the 2012 American conference on Applied Mathematics
Unified forms for Kalman and finite impulse response filtering and smoothing
Automatica (Journal of IFAC)
| Hi-index | 35.69 |
We address a p -shift finite impulse response (FIR) unbiased estimator (UE) for linear discrete time-varying filtering (p=0), p-step prediction (p >; 0), and p-lag smoothing (p <; 0) in state space with no requirements for initial conditions and zero mean noise. A solution is found in a batch form and represented in a computationally efficient iterative Kalman-like one. It is shown that the Kalman-like FIR UE is able to outperform the Kalman filter if the noise covariances and initial conditions are not known exactly, noise is not white, and both the system and measurement noise components need to be filtered out. Otherwise, the errors are similar. Extensive numerical studies of the FIR UE are provided in Gaussian and non-Gaussian environments with outliers and temporary uncertainties.