Kalman filtering : theory and practice using MATLAB / Mohinder S. Grewal, Angus P. Andrews
Tipo de material:
TextoDetalles de publicación: Hoboken : John Wiley & Sons, 2008 Edición: 3rd ed.Descripción: XVI, 575 p. ; 24 cm + 1 disco compactoISBN: 978-0-470-17366-4Tema(s): Kalman, Filtro de | Modelos matemáticosResumen: This is the Third Edition of a successful textbook and professional reference on Kalman filtering theory and applications. Organized for use at the senior undergraduate level and as a first-year, graduate-level course, this book includes real-world problems in practice as illustrative examples and also covers the more practical aspects of implementation. This updated edition includes a number of new problems and chapters.Resumen: Índice: 1. General Information. 2. Linear Dynamic Systems. 3. Random Processes and Stochastic Systems. 4. Linear Optimal Filters and Predictors. 5. Optimal Smoothers. 6. Implementation Methods. 7. Nonlinear Filtering. 8. Practical Considerations. 9. Applications to Navigation. Appendix A. MATLAB Software. Appendix B. Matrix Refresher.
| Tipo de ítem | Biblioteca de origen | Signatura | URL | Estado | Fecha de vencimiento | Código de barras | Reserva de ítems |
|---|---|---|---|---|---|---|---|
| Monografías | 02. BIBLIOTECA CAMPUS PUERTO REAL | 519.218/GRE/kal (Navegar estantería(Abre debajo)) | Texto completo | Disponible Ubicación en estantería | Bibliomaps® | 3744811594 |
Índice
Bibliografía: p. 549-564
This is the Third Edition of a successful textbook and professional reference on Kalman filtering theory and applications. Organized for use at the senior undergraduate level and as a first-year, graduate-level course, this book includes real-world problems in practice as illustrative examples and also covers the more practical aspects of implementation. This updated edition includes a number of new problems and chapters.
Índice: 1. General Information. 2. Linear Dynamic Systems. 3. Random Processes and Stochastic Systems. 4. Linear Optimal Filters and Predictors. 5. Optimal Smoothers. 6. Implementation Methods. 7. Nonlinear Filtering. 8. Practical Considerations. 9. Applications to Navigation. Appendix A. MATLAB Software. Appendix B. Matrix Refresher.
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