声明
Abstract
摘要
TABLE OF CONTENTS
LIST OF FIGURES
LIST OF TABLES
CHAPTER 1 INTRODUCTION
1.1 Overview of ITS and its application in land vehicle localization and navigation
1.2 Vehicular safety application in ITS
1.2.1 Cooperative collision warning system
1.2.2 Current vehicle localization and navigation technology
1.2.3 Global navigation satellites systems(GNSS)
1.2.4 Augmented GNSS
1.2.5 Ultra-wideband(UWB)
1.2.6 Inertial navigation system(INS)
1.3 Cooperative positioning and navigation in VANETs
1.3.1 Challenges of cooperative vehicular positioning
1.3.2 Cooperative vehicular positioning and navigation requirements
1.4 Motivation
1.5 Summary of Contributions
1.5.1 Hybrid GNSS-DGNSS-TOA Cboperative Positioning based on Iterative Finite Difference Particle Filter
1.5.2 GNSS/Low-cost MSMS-INS integration using Variational Bayesian Adaptive Cubature Kalman Smoother and Ensemble Regularized ELM
1.5.3 Hybrid GNSS-UWB Cooperative Positioning using Distributed Randomized Sigma Point Belief Propagation with Asymmetric Generalized Gaussian Mixture
1.6 Organization of the Thesis
CHAPTER 2 BACKGROUND
2.1 Overview of cooperative positioning and integration techniques in VENETs
2.1.1 Received signal strength (RSS)
2.1.2 Radio-ranging techniques
2.1.3 Time-Based Ranging Techniques
2.1.4 Range-rates techniques
2.1.5 Hybrid CP Techniques
2.1.6 Integration techniques
2.2 Background of CP data fusion techniques
2.2.1 Bayesian estimators
2.2.2 Non-Bayesian estimators
2.2.3 Bayesian Inference and Message-Passing
2.3 Background of GNSS/INS integration techniques
2.4 CP Data fusion and integmtion issues in VANETs
2.4.1 CP data fusion issues
2.4.2 GNSS/INS integration issues
2.6 Summary
CHAPTER 3 LITERATURE REVIEW
3.1 CP based on GNSS and radio-ranging positioning technologies
3.1.1 CP based on GNSS and terrestrial radio-ranging positioning technologies
3.1.2 GNSS-UWB CP
3.2 Integration of GNSS with non-radio-ranging positioning technologies
3.2.1 GNSS/INS integration
3.2.2 GNSS/DR integration
3.2.3 GNSS/other non-radio ranging sensors integration
3.3 Cooperative positioning data Fusion techniques
3.3.1 Probabilistic based CP data fusion techniques
3.3.2 Filtering based CP data fusion techniques
3.4 GNSS/INS integration techniques
3.6 Summary
CHAPTER 4 HYBRID GNSS-DGNSS-TOA COOPERATIVE POSITIONING BASED ON ITERATVE FINITE DIFFERENCE PARTICLE FILTER
4.1 Introduction
4.2 Definitions
4.2.1 System model
4.2.2 Sensor observation model
4.3.Proposed algorithms
4.3.1 Likelihood function
4.3.2 Finite difference particle filter
4.2.3 Proposed hybrid cooperative positioning approach
4.2.4 Complexity analysis and comparison
4.4 Simulation Results
4.5 Summary
CHAPTER 5 GNSS/LOW-COST MEMS-INS INTEGRATION USING VARIATIONAL BAYESIAN ADAPTIVE CUBATURE KALMAN SMOOTHER AND ENSEMBLE REGULAIUZED ELM
5.1 Introduction
5.2 Proposed Algorithms
5.2.1 Parameters computation
5.2.2 MEM-INS measurements model
5.2.3 Variational Bayesian adaptive cubature Kalman smoother
5.2.3 Ensemble RELM based Ada Boost-R.T
5.2.3 GNSS/INS integration scheme
5.2.4 Prediction process
5.3 Experimental results
5.3.1 Parameters of the GPS/INS integrated navigation model
5.3.2 Results and discussion
5.4 Summary
CHAPTER 6 HYBRID GNSS-UWB COPERATIVE POSITIONING USING DISTRIBUTED RANDOMIZED SIGMA POINT BELIEF PROPAGATION WITH ASYMMETRIC GENERALIZED GAUSSIAN MIXTURE
6.1 Introduction
6.2 System model and problem formulation
6.3 Proposed Algorithms
6.3.1 Bayesian Inference using Distributed Belief Propagation
6.3.2 Ranging error model based on asymmetric generalized Gaussian mixture
6.3.3 Model selection through minimum message length
6.3.4 Intrinsic and extrinsic messages computation
6.3.5 Randomized sigma-point approximation based on stochastic integration rule
6.4 Simulation Results and Discussion
6.5 Summary
CHAPTER 7 CONCLUSIONS AND FUTURE WORK
REFERENCES
ACKNOWLEDGEMENTS
APPENDIX