Nonlinear Parameter Estimation
โ Scribed by Yonathan Bard
- Year
- 1973
- Tongue
- English
- Leaves
- 175
- Category
- Library
No coin nor oath required. For personal study only.
๐ SIMILAR VOLUMES
In this book two probabilistic model-based estimators are introduced that allow the reconstruction and identification of space-time continuous physical systems. The Sliced Gaussian Mixture Filter (SGMF) exploits linear substructures in mixed linear/nonlinear systems, and thus is well-suited for iden
The first comprehensive development of Bayesian Bounds for parameter estimation and nonlinear filtering/trackingBayesian estimation plays a central role in many signal processing problems encountered in radar, sonar, communications, seismology, and medical diagnosis. There are often highly nonlinear
In this book two probabilistic model-based estimators are introduced that allow the reconstruction and identification of space-time continuous physical systems. The Sliced Gaussian Mixture Filter (SGMF) exploits linear substructures in mixed linear/nonlinear systems, and thus is well-suited for iden
For the last decade, various simulation-based nonlinear and non-Gaussian filters and smoothers have been proposed. In the case where the unknown parameters are included in the nonlinear and non-Gaussian system, however, it is very difficult to estimate the parameters together with the state variable