Ch 46. GNSS-INS Integration
Andrey Soloviev, James Farrell, Maarten Uijt de Haag
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Chapter Table of Contents
Chapter Table of Contents
46.1 Main Principles of Inertial Navigation
46.2 Inertial Error Propagation
46.2.1 One-Dimensional Case
46.2.2 Sensor Error Models for Three-Dimensional Case
46.2.3 Error Propagation Through Attitude Computation
46.2.4 Error Propagation Through Coordinate Transformation
46.2.5 Error Propagation Through Integration
46.2.6 Bringing It All Together
46.3 Loose Integration: Solution-Domain Sensor Fusion
46.3.1 Position Updates
46.3.2 Attitude Updates
46.3.3 Example Simulation Scenario
46.4 Tight Integration: Measurement-Domain Sensor Fusion
46.4.1 Pseudorange Updates
46.4.2 Carrier-Phase Updates
46.5 Deep Integration: Sensor Fusion at the Signal Processing Level
46.5.1 Example Implementation with Long Coherent Integration of GPS Signals
46.5.2 Computation of Dynamic Reference Trajectory
46.5.3 Error-State Estimation
46.5.4 Treatment of Navigation Data Bits
46.6 Implementation Case Studies
46.6.1 GNSS/INS Integration for Navigation in Urban Environments
46.6.2 Deep GPS/INS Integration for Navigation Under Dense Foliage
46.7.1 Integration Approach
46.7.1.1 Role of the IMU Reappraised
46.7.1.2 Basis for Key Steps
46.7.1.3 Specific Topology and Advantages
46.7.2 Algorithms
46.7.2.1 Position Incrementing
46.7.2.2 Velocity Incrementing
46.7.2.3 Attitude Incrementing
46.7.3 Error and Covariance Propagation
46.7.4 Measurement Differencing
46.7.4.1 Differencing Across Satellites
46.7.4.2 Differencing Across Receivers
46.7.4.3 Differencing Across Time
46.7.4.4 Correlations
46.7.5 One-Second Change in Carrier Phase
46.7.6 Formation of Residuals
46.7.7 Formation of Sensitivities
46.7.8 Integrity Testing
46.7.9 GPS/INS Flight Test
46.7.9.1 Time Histories
46.7.9.2 Data-Edit Performance
A. Appendix
References
Chapter Overview:
Part 1 discusses fundamentals of GNSS/INS. This part introduces key concepts and their applicability for real-world application scenarios (such as a GNSS/MEMS integration case study). Development of integrated mechanizations is presented in such a way that the main principles are upheld but without overwhelming mathematical complexity. For example, a one-dimensional INS error propagation model is considered in Section 46.2 to introduce you to INS state transition models. The material in Part 1 is organized as follows. Sections 46.1 and 46.2 review the main principles of inertial navigation and develop basic INS error propagation models, respectively. Sections 46.3 through 46.5 describe loosely, tightly, and deeply coupled navigation mechanization. Section 46.6 presents two case studies including (i) GNSS/INS integration for urban navigation, which includes the use of consumer-grade MEMS inertial sensors, and (ii) deep integration for navigation in dense forestry areas.
Part 2 describes GNSS and inertial data used to extract maximum dynamic information. Highly accurate dynamics give estimates a realistic start when satnav fades but, aside from that, extended GNSS-denied durations are beyond scope here. Other references cover that and additional important topics (e.g., nonlinear methods; visual odometry). Just integration, contingent on having at least marginal amounts of data to combine, allowed ample range for revisionist approaches: Fundamental design methodology redefinition for robustness and flexibility. Ability to quantify the relation of process noise to data averaging duration. Simple program proving that ability with block/sequential correspondence. State vector and covariance propagation: what can/can't be simplified. Reduction of complex dynamics to three ultrasimple submodels. Dynamics by carrier phase, position by pseudorange. "Bulletproof" ambiguous fragmented 1-sec phase changes interoperable, no mask. Inertial, satnav, differencing, EKF, and integrity algorithms provided. Cm/sec RMS velocity and state-of-the-art leveling validated with flight data.
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Figure 46.18 Deeply integrated GNSS/INS implementation
Figure 46.18 Deeply integrated GNSS/INS implementation
Figure 46.38 Segmented estimator with updates
Figure 46.38 Segmented estimator with updates
Table 46.1 Terms contributing to carrier-phase difference residuals
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