Loosely-Coupled GNSS-INS Integration with Speed Scale-Factor and Heading Bias Calibration
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Solution Overview
Problem
Low-cost inertial sensors used in Global Navigation Satellite System (GNSS) integration face challenges in providing accurate navigation data, especially in pedestrian applications, due to inaccuracies in speed and heading measurements, and assumptions of full calibration and precise measurements, which are not always feasible.
Innovation Solution
A loosely-coupled integration filter, based on an Extended Kalman Filter (EKF), combines GNSS and INS navigation information, estimating and compensating for speed scale-factor and heading bias, and adapts to changing GNSS signal conditions using reliability metrics, allowing the use of low-cost inertial sensors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If low-cost inertial sensors are used in GNSS integration, then cost is reduced, but measurement precision deteriorates due to inaccuracies in speed and heading measurements
Solution Approach 1:
The integration filter continuously compares INS navigation information with GNSS navigation information and uses the differences to estimate and compensate for speed scale-factor and heading bias errors in real-time, creating a feedback loop that corrects measurement inaccuracies
Solution Approach 2:
The system dynamically estimates and adjusts calibration parameters (speed scale-factor and heading bias) based on the comparison between INS and GNSS measurements, transforming the fixed erroneous parameters into variable parameters that can be corrected through the filter
2Measurement precision
If full calibration and precise measurements are assumed for inertial sensors, then measurement precision is improved, but device complexity increases due to calibration requirements
Solution Approach 1:
The system performs self-calibration by using the available GNSS measurements to automatically estimate and correct INS errors through the integration filter, eliminating the need for external calibration equipment or procedures
Solution Approach 2:
The filter continuously performs error estimation and compensation in advance to maintain accurate navigation information, preparing corrected data before it is needed for final navigation solutions
3Measurement precision
If GNSS and INS are tightly integrated, then navigation accuracy is improved, but device complexity increases due to integration requirements
Solution Approach 1:
The system implements a loosely-coupled integration that performs partial correction of INS errors by focusing on the most significant error sources (speed scale-factor and heading bias) rather than attempting to correct all possible errors, achieving sufficient accuracy without excessive complexity
Data Source
AI summary
Techniques for loosely coupling a Global Navigation Satellite System (“GNSS”) and an Inertial Navigation System (“INS”) integration are disclosed herein. A system includes a GNSS receiver, an INS, and an integration filter coupled to the GNSS receiver and the INS. The GNSS receiver is configured to provide GNSS navigation information comprising GNSS receiver position and/or velocity estimates. The INS is configured to provide INS navigation information based on an inertial sensor output. The integration filter is configured to provide blended position information comprising a blended position estimate and/or a blended velocity estimate by combining the GNSS navigation information and the INS navigation information, and to estimate and compensate at least one of a speed scale-factor and a heading bias of the INS navigation information.