Optimal Estimation of Dynamic SystemsCRC Press, 27.04.2004 - 608 Seiten Most newcomers to the field of linear stochastic estimation go through a difficult process in understanding and applying the theory.This book minimizes the process while introducing the fundamentals of optimal estimation. Optimal Estimation of Dynamic Systems explores topics that are important in the field of control where the signals received are used to determine highly sensitive processes such as the flight path of a plane, the orbit of a space vehicle, or the control of a machine. The authors use dynamic models from mechanical and aerospace engineering to provide immediate results of estimation concepts with a minimal reliance on mathematical skills. The book documents the development of the central concepts and methods of optimal estimation theory in a manner accessible to engineering students, applied mathematicians, and practicing engineers. It includes rigorous theoretial derivations and a significant amount of qualitiative discussion and judgements. It also presents prototype algorithms, giving detail and discussion to stimulate development of efficient computer programs and intelligent use of them. This book illustrates the application of optimal estimation methods to problems with varying degrees of analytical and numercial difficulty. It compares various approaches to help develop a feel for the absolute and relative utility of different methods, and provides many applications in the fields of aerospace, mechanical, and electrical engineering. |
Inhalt
1 Least Squares Approximation | 1 |
2 Probability Concepts in Least Squares | 63 |
3 Review of Dynamical Systems | 119 |
Applications | 189 |
5 Sequential State Estimation | 243 |
6 Batch State Estimation | 343 |
Applications | 411 |
8 Optimal Control and Estimation Theory | 471 |
A Matrix Properties | 533 |
B Basic Probability Concepts | 553 |
C Parameter Optimization Methods | 569 |
D Computer Software | 585 |
| 587 | |
Andere Ausgaben - Alle anzeigen
Optimal Estimation of Dynamic Systems John L. Crassidis,John L. Junkins Eingeschränkte Leseprobe - 2004 |
Häufige Begriffe und Wortgruppen
aircraft algorithm angle applications approach approximation assumed attitude backward chapter clearly computed Consider constant continuous-time covariance defined definite denoted derived desired determine developed differential discrete-time discussed dynamics eigenvalues elements equation error estimate estimation theory example exercise expression extended Kalman filter function gain Gaussian given given by eqn gives Global Positioning System identity input integrated interval inverse involves Kalman filter known leads least squares linear matrix mean measurement methods minimization motion nonlinear Note observation obtained optimal orbit orthogonal orthogonal matrix parameters performance position present probability problem process noise propagation prove quaternion reduces reference respect sampling shown simple simulation smoother solution solving spacecraft stability standard steady-state Substituting eqn Table Taking theory tion true update variable variance various vector yields zero