Maximum likelihood estimation using square root information filters |
| |
Abstract: | The maximum likelihood parameter estimation algorithm is known to provide optimal estimates for linear time-invariant dynamic systems. However, the algorithm is computationally expensive and requires evaluations of the gradient of a log likelihood function and the Fisher information matrix. By using the square-root information filter, a numerically reliable algorithm to compute the required gradient and the Fisher information matrix is developed. The algorithm is a significant improvement over the methods based on the conventional Kalman filter. The square-root information filter relies on the use of orthogonal transformations that are well known for numerical reliability. This algorithm can be extended to real-time system identification and adaptive control |
| |
Keywords: | |
|
|