Article ID: | iaor20031135 |
Country: | United Kingdom |
Volume: | 22 |
Issue: | 3 |
Start Page Number: | 151 |
End Page Number: | 156 |
Publication Date: | May 2001 |
Journal: | Optimal Control Applications & Methods |
Authors: | Yaz Edwin Engin, Yaz Yvonne Ilke |
Keywords: | Kalman filter |
In a continuous-time Kalman filter, it is required that the measurement noise covariance be non-singular. If the measurements are noise-free, then this condition does not hold and, in practice, the measurement data are differentiated to define a derived measurement function to build what is known as Deyst filter. It is proposed here that a reduced-order observer be used in deriving the linear minimum-variance filter to construct state estimates based on the original measurement data with no need for differentiation. This filter is of dimension (