UID:
almahu_9948025517602882
Format:
1 online resource (563 p.)
ISBN:
1-281-01893-7
,
9786611018931
,
0-08-054173-9
Content:
This book presents fundmentals of orbit determination--from weighted least squares approaches (Gauss) to today's high-speed computer algorithms that provide accuracy within a few centimeters. Numerous examples and problems are provided to enhance readers' understanding of the material.*Covers such topics as coordinate and time systems, square root filters, process noise techniques, and the use of fictitious parameters for absorbing un-modeled and incorrectly modeled forces acting on a satellite. *Examples and exercises serve to illustrate the principles throughout each chapter. 〈br
Note:
Description based upon print version of record.
,
Front Cover; Statistical Orbit Determination; Copyright Page; Contents; Preface; Chapter 1. Orbit Determination Concepts; 1.1 Introduction; 1.2 Uniform Gravity Field Model; 1.3 Background and Overview; 1.4 Summary; 1.5 References; 1.6 Exercises; Chapter 2. The Orbit Problem; 2.1 Historical Background; 2.2 Problem of Two Bodies: General Properties; 2.3 Perturbed Motion; 2.4 Coordinate Systems and Time: Introduction; 2.5 Orbit Accuracy; 2.6 References; 2.7 Exercises; Chapter 3. Observations; 3.1 Introduction; 3.2 Observations; 3.3 Conceptual Measurement Systems; 3.4 Realization of Measurements
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3.5 Measurement Systems3.6 Differenced Measurements; 3.7 Satellite Positions; 3.8 Angles; 3.9 References; 3.10 Exercises; Chapter 4. Fundamentals of Orbit Determination; 4.1 Introduction; 4.2 Linearization of the Orbit Determination Process; 4.3 The Least Squares Solution; 4.4 The Minimum Variance Estimate; 4.5 Maximum Likelihood and Bayesian Estimation; 4.6 Computational Algorithm for the Batch Processor; 4.7 The Sequential Estimation Algorithm; 4.8 Example Problems; 4.9 State Noise Compensation Algorithm; 4.10 Information Filter; 4.11 Batch and Sequential Estimation; 4.12 Observability
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4.13 Error Sources4.14 Orbit Accuracy; 4.15 Smoothing; 4.16 The Probability Ellipsoid; 4.17 Combining Estimates; 4.18 References; 4.19 Exercises; Chapter 5. Square Root Solution Methods; 5.1 Introduction; 5.2 Cholesky Decomposition; 5.3 Least Squares Solution via Orthogonal Transformation; 5.4 Givens Transformations; 5.5 The Householder Transformation; 5.6 Numerical Examples; 5.7 Square Root Filter Algorithms; 5.8 Time Update of the Estimation Error Covariance Matrix; 5.9 Continuous State Error Covariance Propagation; 5.10 The Square Root Information Filter
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5.11 Process Noise Parameter Filtering/Smoothing Using a SRIF5.12 References; 5.13 Exercises; Chapter 6. Consider Covariance Analysis; 6.1 Introduction; 6.2 Bias in Linear Estimation Problems; 6.3 Formulation of the Consider Covariance Matrix; 6.4 The Sensitivity and Perturbation Matrices; 6.5 Inclusion of Time-Dependent Effects; 6.6 Propagation of the Error Covariance; 6.7 Sequential Consider Covariance Analysis; 6.8 Example: Freely Falling Point Mass; 6.9 Example: Spring-Mass Problem; 6.10 Errors in the Observation Noise and A Priori State Covariances
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6.11 Errors in Process Noise, Observation Noise, and State Covariance6.12 Covariance Analysis and Orthogonal Transformations; 6.13 References; 6.14 Exercises; Appendix A. Probability and Statistics; A.1 Introduction; A.2 Axioms of Probability; A.3 Conditional Probability; A.4 Probability Density and Distribution Functions; A.5 Expected Values; A.6 Examples and Discussion of Expectation; A.7 Moment Generating Functions; A.8 Some Important Continuous Distributions; A.9 Two Random Variables; A.10 Marginal Distributions; A.11 Independence of Random Variables; A.12 Conditional Probability
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A.13 Expected Values of Bivariate Functions
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English
Additional Edition:
ISBN 0-12-683630-2
Language:
English
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