Zhou, Junchuan: Low-cost MEMS-INS/GPS integration using nonlinear filtering approaches. 2013
Inhalt
- Acknowledgments
- Contents
- List of Figures
- List of Tables
- Acronyms
- Abstract
- Kurzfassung
- Outline
- Motivation
- 1. INS/GPS Integration Principles
- 1.1 Introduction
- 1.2 GPS data processing
- 1.3 INS principle
- 1.4 INS/GPS integration
- 1.5 Field experiment
- 1.6 Summary
- 2. Nonlinear Filtering Methods
- 2.1 Introduction
- 2.2 Basics in probability theory
- 2.3 Recursive Bayesian state estimator
- 2.4 Recursive Bayesian state estimator with Gaussian assumptions
- 2.5 Unscented Kalman filter
- 2.6 Particle filter
- 2.7 Unscented particle filter
- 2.8 Simulation test
- 2.9 Summary
- 3. INS/GPS using Quaternion-based Nonlinear Filtering Methods
- 3.1 Introduction
- 3.2 Quaternion-based INS/GPS using Extended Kalman filter
- 3.3 Quaternion-based INS/GPS using Unscented Kalman filter
- 3.4 Quaternion-based INS/GPS using Unscented Particle filter
- 3.5 Summary
- 4. INS/GPS Tightly-coupled Integration using Sequential Processing
- 4.1 Introduction
- 4.2 Velocity determination
- 4.3 Augmentation of system state vector (1st Method)
- 4.4 Backward prediction of delay states by current states (2nd method)
- 4.5 Comparisons of two approaches
- 4.6 Simulation setup
- 4.7 Numerical result
- 4.8 Summary
- 5. Summary and Conclusions
- Appendix A: Basics on Quaternions
- Appendix B: Transformation of Quaternion Covariance to Euler Angle Covariance
- Appendix C: Calculation of Matrix Inversion using Gauss-Jordan Elimination Method
- Appendix D: Sequential Measurement Update using Joseph Covariance Update Formula
- Bibliography