Optimal Estimation of Dynamic Systems
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 receiv
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Least Squares Approximation
Probability Concepts in Least Squares
Review of Dynamical Systems
Parameter Estimation Applications
Sequential State Estimation
Batch State Estimation
Estimation of Dynamic Systems Applications
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aircraft algorithm angle applications approach approximation assumed attitude backward chapter clearly computed Consider constant continuous-time covariance defined definite denoted derived determine developed differential discrete-time discussed dynamics eigenvalues elements equation error estimate estimation theory example exercise expression extended Kalman filter final function gain Gaussian given given by eqn gives identity 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 satisfied 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