Filter error method
Jitendra R. Raol, G Girija, Jatinder Singh · Institution of Engineering and Technology eBooks · 2011
The filter error method is the most general approach to parameter estimation that accounts for both the process and the measurement noise. The method was first studied in Reference 1 and since then, various applications of the techniques to estimate parameters from measurements with turbulence (accounting for process noise) have been reported. As mentioned before, the algorithm includes a state estimator (Kalman filter) to obtain filtered data from noisy measurements. Three different ways to account for process noise in a linear system have been suggested. All these formulations use the modified Gauss-Newton optimisation to estimate the system parameters and the noise statistics. The major difference among these formulations is the manner in which the noise covariance matrices are estimated. A brief insight into the formulations for linear systems is provided next.