Ch 46. GNSS-INS Integration

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.

On this page you can:

• Get hi-res copies of selected figures from the chapter, for use with attribution.

• Give feedback.