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Bayesian Estimation and Tracking [2012]
- 1
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Introduction
- 11
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Preliminary Mathematical Concepts
- 42
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General Concepts of Bayesian Estimation
- 56
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Case Studies: Preliminary Discussions
- 71
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The Gaussian Noise Case: Multidimensional Integration of Gaussian‐Weighted Distributions
- 86
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The Linear Class of Kalman Filters
- 93
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The Analytical Linearization Class of Kalman Filters: The Extended Kalman Filter
- 115
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The Sigma Point Class: The Finite Difference Kalman Filter
- 128
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The Sigma Point Class: The Unscented Kalman Filter
- 140
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The Sigma Point Class: The Spherical Simplex Kalman Filter
- 148
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The Sigma Point Class: The Gauss–Hermite Kalman Filter
- 164
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The Monte Carlo Kalman Filter
- 168
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Summary of Gaussian Kalman Filters
- 176
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Performance Measures for the Family of Kalman Filters
- 199
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Introduction to Monte Carlo Methods
- 218
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Sequential Importance Sampling Particle Filters
- 247
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The Generalized Monte Carlo Particle Filter
- 257
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A Spherical Constant Velocity Model for Target Tracking in Three Dimensions
- 308
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Tracking a Falling Rigid Body Using Photogrammetry
- 346
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Sensor Fusion Using Photogrammetric and Inertial Measurements
- 367
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Index
- i
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Frontmatter