MEMS-Based Integrated Navigation
1st Edition
Published on 1. January 2010
208 pages
978-1-60807-044-2 (ISBN)
Description
Due to the micro-scale size and low power consumption of Microelectromechanical systems (MEMS) are now being utilized in a variety of fields. This leading-edge resource focuses on the application of MEMS inertial sensors to navigation systems. The book shows you how to minimize cost by adding and removing inertial sensors. Moreover, this practical reference provides you with various integration strategies with examples from real field tests. From an introduction to MEMS navigation related applications... to special topics on Alignment for MEMS-Based Navigation... to discussions on the Extended Kalman Filter, this comprehensive book covers a wide range of critical topics in this fast-growing area.
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Priyanka Aggarwal | Naser El-Sheimy | Aboelmagd Noureldin
MEMS-Based Integrated Navigation
Book
08/2010
Artech House Publishers
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Content
- MEMS-Based Integrated Navigation
- Contents
- Preface
- 1 Microelectromechanical Systems (MEMS)
- 1.1 Introduction
- 1.2 Different Applications of MEMS Devices
- 1.2.1 Electric Wheelchairs
- 1.2.2 Personnel Tracking and Navigation
- 1.2.3 Agriculture
- 1.2.4 Event Data Recorder
- 1.2.5 Wildlife and Livestock Tracking
- 1.2.6 Patient Monitoring
- 1.2.7 Electronic Stability Control
- 1.2.8 Supplemental/Secondary Restraint System
- 1.2.9 Land Vehicle Navigation
- 1.3 Aided MEMS-Based Inertial Navigation
- 1.3.1 Aiding Sources in Coordinate Domain
- 1.3.2 Aiding Sources in Velocity Domain
- 1.3.3 Aiding Sources in Attitude Domain
- References
- 2 MEMS Inertial Sensors
- 2.1 Introduction
- 2.2 Accelerometers
- 2.2.1 Working Principle for MEMS Accelerometers
- 2.2.2 Classifications of Accelerometers
- 2.3 Gyroscopes
- 2.3.1 Principle of MEMS Gyroscopes
- 2.3.2 Classification of MEMS Gyroscopes
- 2.4 MEMS Inertial Sensors for the Most Economical Land Navigation
- 2.5 Method to Compute Minimum Sensors
- 2.6 Results and Analysis
- 2.6.1 Drift Errors Without NHC
- 2.6.2 Drift Errors with NHC
- References
- 3 MEMS Inertial Sensors Errors
- 3.1 Introduction
- 3.2 Systematic Errors
- 3.2.1 Bias
- 3.2.2 Input Sensitivity or Scale Factor
- 3.2.3 Nonorthogonality/ Misalignment Errors
- 3.2.4 Run-to-Run (Repeatability) Bias/Scale Factor
- 3.2.5 In Run (Stability) Bias/Scale Factor
- 3.2.6 Temperature-Dependent Bias/Scale Factor
- 3.3 Calibration of Systematic Sensor Errors
- 3.3.1 6-Position Static Test
- 3.3.2 Angular Rate Test
- 3.3.3 Thermal Calibration Test
- 3.4 Random/Stochastic Errors
- 3.4.1 Examples of Random Processes
- 3.5 Stochastic Modeling
- 3.5.1 Autocorrelation Function
- 3.5.2 Allan Variance Methodology
- 3.6 Sensors Measurement Models
- 3.6.1 Accelerometer Measurement Model
- 3.6.2 Gyroscope Measurement Model
- References
- 4 Initial Alignment ofMEMS Sensors
- 4.1 Introduction
- 4.2 Considerations for MEMS Sensor Navigation
- 4.3 Portable Navigation System
- 4.4 Economical Considerations
- 4.4.1 Economically Desirable Configuration
- 4.4.2 Complete Six DOF IMU-Economically Less Desirable
- 4.5 Absolute Alignment
- 4.5.1 Static Alignment for MEMS Sensors
- 4.5.2 Static Alignment Example
- 4.6 Velocity Matching Alignment
- 4.6.1 GPS Derived Heading Example
- 4.7 Transfer Alignment
- References
- 5 Navigation Equations
- 5.1 Introduction-Mathematical Relations and Transformations Between Frames
- 5.1.1 e-Frame to i-Frame
- 5.1.2 ENU l-Frame to e-Frame
- 5.1.3 NED l-Frame to e-Frame
- 5.1.4 b-Frame to ENU l-Frame
- 5.1.5 b-Frame to NED l-Frame
- 5.2 Motion Modeling in the l-Frame
- 5.2.1 ENU Realization
- 5.2.2 NED Realization
- 5.3 Solving Mechanization Equations
- 5.3.1 Classical Method
- 5.3.2 Half-Interval Method
- References
- 6 Aiding MEMS-Based INS
- 6.1 Introduction
- 6.1.1 Loosely Coupled Mode of Integration
- 6.1.2 Tightly Coupled Mode of Integration
- 6.2 Introduction to Kalman Filter
- 6.2.1 Dynamic Model
- 6.2.2 Measurement Model
- 6.3 Kalman Filter Algorithm
- 6.3.1 The Prediction Stage
- 6.3.2 The Update Stage
- 6.4 Introduction to Extended Kalman Filter
- 6.4.1 Linearization
- 6.4.2 EKF Limitations
- References
- 7 Artificial Neural Networks
- 7.1 Introduction
- 7.2 Types of ANNs
- 7.2.1 Multilayer Perception Neural Network (MLPNN)
- 7.2.2 Radial Basis Function Neural Network (RBFNN)
- 7.2.3 Adaptive Neuro Fuzzy Inference System (ANFIS)
- 7.3 Whole Navigation States Architecture
- 7.3.1 Example of Position Update Architecture
- 7.3.2 Example of Position and Velocity Update Architecture
- 7.4 Navigation Error States Architecture
- 7.4.1 Architecture for INS/GPS Integration
- 7.4.2 System Implementation
- 7.4.3 Combined P - dP and V - dV Architecture for INS/GPS
- 7.4.4 ANN/KF Augmented Module for INS/GPS Integration
- References
- 8 Particle Filters
- 8.1 Introduction
- 8.2 The Monte Carlo Principle
- 8.3 Importance Sampling Method
- 8.4 Resampling Methods
- 8.4.1 Simple Random Resampling
- 8.4.2 Systematic Resampling (SR)
- 8.4.3 Stratified Resampling
- 8.4.4 Residual Resampling
- 8.5 Basic Particle Filters
- 8.6 Types of Particle Filters
- 8.6.1 Extended Particle Filter (EPF) and Unscented Particle Filter (UPF)
- 8.6.2 Rao-Blackwellized Particle Filter (RBPF)
- 8.6.3 Likelihood Particle Filter (LPF)
- 8.6.4 Regularized Particle Filter (RPF)
- 8.6.5 Gaussian Particle Filter (GPF) and Gaussian Sum Particle Filter
- 8.7 Hybrid Extended Particle Filter (HEPF)
- 8.7.1 Zero Velocity Condition Detection Algorithm
- 8.7.2 Algorithm of the Hybrid Extended Particle Filter
- 8.7.3 HEPF Results
- 8.7.4 Partial Sensor Configuration
- References
- Appendix A Linearization Process for the EKF forLow-Cost Navigation
- A.1 System Model for Loosely Coupled Approach
- A.1.1 Attitude Errors
- A.1.2 Velocity Linearization
- A.1.3 Position Linearization
- A.1.4 Sensor Errors
- A.2 GPS Measurement Model
- A.3 System Model for the Tightly Coupled Approach
- A.4 The Update Stage
- A.5 Pseudorange and Doppler Corrections
- References
- About the Authors
- Index
