E-Books durchsuchen

Bayesian Estimation and Tracking [2012]

1
Introduction
11
Preliminary Mathematical Concepts
42
General Concepts of Bayesian Estimation
56
Case Studies: Preliminary Discussions
71
The Gaussian Noise Case: Multidimensional Integration of Gaussian‐Weighted Distributions
86
The Linear Class of Kalman Filters
93
The Analytical Linearization Class of Kalman Filters: The Extended Kalman Filter
115
The Sigma Point Class: The Finite Difference Kalman Filter
128
The Sigma Point Class: The Unscented Kalman Filter
140
The Sigma Point Class: The Spherical Simplex Kalman Filter
148
The Sigma Point Class: The Gauss–Hermite Kalman Filter
164
The Monte Carlo Kalman Filter
168
Summary of Gaussian Kalman Filters
176
Performance Measures for the Family of Kalman Filters
199
Introduction to Monte Carlo Methods
218
Sequential Importance Sampling Particle Filters
247
The Generalized Monte Carlo Particle Filter
257
A Spherical Constant Velocity Model for Target Tracking in Three Dimensions
308
Tracking a Falling Rigid Body Using Photogrammetry
346
Sensor Fusion Using Photogrammetric and Inertial Measurements
367
Index
i
Frontmatter
Feedback