Gyro-Free Inertial Navigation Technology/Технология инерциальной навигации без гироскопа

Издание на английском языке
Inertial navigation is an essential modern navigation method based on Newton’s laws of motion. Gyroscopes and accelerometers are used to measure the angular and linear motion parameters of a moving vehicle. The position and velocity, as well as the heading and attitude of the vehicle, are then determined by dead reckoning if the initial conditions, such as position and velocity of the vehicle, are well known.

Contents
1 Introduction
1.1 Inertial Navigation
1.2 Gyro-Free Inertial Navigation
1.3 Brief Introduction to the Book
References
2 Gyro-Free Inertial Navigation Principle
2.1 Accelerometer Working Principle
2.2 Rigid Body’s Motion Model
2.3 Vehicle’s Angular Rate Resolution
2.3.1 Angular Acceleration Resolution
2.3.2 Square of Angular Rate Resolution
2.3.3 Cross Product Between Two Different Axis Rate Resolution
2.3.4 Other Angular Rate Resolution Methods
2.4 Accelerometer Setting Scheme
2.4.1 Classic Six-Accelerometer Setting Scheme
2.4.2 Typical Nine-Accelerometer Setting Scheme
2.4.3 A Twelve-Accelerometer Setting Scheme
2.5 GFIMU Protype
References
3 Initial Alignment of GFINS
3.1 Independent Self-Alignment
3.1.1 Feasibility of Applying SINS Alignment Method to GFINS
3.1.2 Single-Axis Rotation-Based Self-Alignment for GFINS
3.1.3 Initial Alignment Accuracy Analysis
3.1.4 Simulation
3.2 External-Information-Aided GFINS Initial Alignment
3.2.1 External-Information-Aided Initial Alignment Model
3.2.2 Simulation
References
4 Attitude Resolution of GFINS
4.1 Direction Cosine Method
4.2 Euler Angle Method
4.3 Quaternion Method
4.3.1 Rotation Vector and Quaternion
4.3.2 Quaternion and Direction Cosine Matrix
4.4 Quaternion Based Attitude Resolution Algorithm
4.4.1 Attitude Resolution Algorithm
4.4.2 Rotation Vector Resolution
4.4.3 Update Quaternion Unitization
4.4.4 Attitude Resolution
4.4.5 Scrumming-Effect Offset
4.5 Attitude Resolution Accuracy Analysis
4.5.1 Cone-Effects
4.5.2 Quaternion Calculation Error
4.6 Experiment
References
5 Accelerometer Noise Characteristics Analysis and Denoising
5.1 Accelerometer Noise Characteristics Analysis and Processing
5.1.1 Accelerometer Noise Statistical Characteristics
5.1.2 Allan Variance-Based Analysis of Accelerometer Noise
5.2 Modified Adaptive Kalman Filter Denoising
5.2.1 New Information-Based Noise Adaptive Estimation
5.2.2 Optimization Estimation of Sliding Window’s Width
5.3 Wavelet-Based Kalman Filter for Accelerometer Denoising
5.3.1 Online Approximation Estimation of Observation Noise
5.3.2 Kalman Filter Equation Under Non-standard Observation Noise
5.4 Experiments
5.4.1 Accelerometer Denoising Model in GFINS
5.4.2 New Information-Based Self-Adaptive Kalman Filter Denoising
5.4.3 Wavelet Kalman Filter Denoising
References
6 Accelerometer Mounting Error Calibration
6.1 Accelerometer Mounting Error Analysis
6.2 Accelerometer Mounting Error Calibration Principle
6.2.1 General Calibration Method of Accelerometer Mounting Error
6.3 A Simple Method to Calibrate Accelerometer Mounting Error
6.4 Simulation
6.4.1 Simulation of General Method of Accelerometer Mounting Error Calibration
6.4.2 Simulation of Simplified Method to Calibrate Accelerometer Mounting Error
6.5 Accelerometer Mounting Error Estimation Test
6.6 Accelerometer Mounting Error Calibration Test
6.6.1 Accelerometer Output Error Calculation
6.6.2 GFIMU Navigation Parameters Resolution Error Calibration
References
7 GPS-Aided Integrated Navigation with GFINS
7.1 Introduction
7.1.1 Cascade Integration
7.1.2 Loosely Coupled Integration
7.1.3 Tightly Coupled Integration
7.2 GPS/GFINS Non-linear Integration Model
7.2.1 System State Equation
7.2.2 System Observation Equation
7.3 GPS/GFINS Non-linear Integration Filter
7.3.1 EKF Filter Algorithm
7.3.2 UKF Algorithm
7.3.3 PF Filter
7.3.4 PF-Based GPS/GFINS Integration Filter
7.4 GPS/GFIMU Integration Navigation Experiment
References
Appendix A: Cone-Effect Calculation