Ch 46. GNSS-INS Integration

Andrey Soloviev, James Farrell, Maarten Uijt de Haag

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 Role of the IMU Reappraised Basis for Key Steps Specific Topology and Advantages

46.7.2 Algorithms Position Incrementing Velocity Incrementing Attitude Incrementing

46.7.3 Error and Covariance Propagation

46.7.4 Measurement Differencing Differencing Across Satellites Differencing Across Receivers Differencing Across Time 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 Time Histories Data-Edit Performance

A. Appendix


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 46.38 Segmented estimator with updates

Table 46.1 Terms contributing to carrier-phase difference residuals