Tightly-Coupled GNSS IMU Blending Filter Calibration
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Solution Overview
Problem
Global navigation satellite system (GNSS) receivers experience performance degradation due to signal blockage, attenuation, and multipath issues, particularly in indoor and urban environments, and current inertial measurement units (IMUs) with low-cost sensors fail to provide accurate navigation data, leading to significant navigation errors in speed and heading biases.
Innovation Solution
A tightly-coupled blending filter based on the extended Kalman filter (EKF) is implemented to integrate IMU navigation data with GNSS measurements, adding states for estimating and compensating speed and heading biases, and includes calibration features to improve navigation accuracy without feedback loops.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If low-cost inertial sensors are used in IMU, then cost is reduced, but navigation accuracy deteriorates due to significant speed and heading biases
Solution Approach 1:
The patent implements a feedback mechanism where the integration filter continuously monitors GNSS-derived position and velocity, compares them with IMU-based estimates, and uses the discrepancies to adaptively adjust and recalibrate the IMU sensor characteristics in real-time. This closed-loop feedback enables the system to compensate for IMU biases using available GNSS data, thereby maintaining navigation accuracy despite using low-cost sensors.
Solution Approach 2:
The patent dynamically changes the operational parameters of the integration filter based on signal availability and quality. When GNSS signals are available, the filter adjusts its calibration parameters to leverage GNSS data for correcting IMU biases. When GNSS signals are unavailable, the system switches to using IMU data with adjusted bias parameters, allowing the system to adapt to varying operational conditions and maintain accuracy across different environments.
2Productivity
If GNSS receivers operate in indoor and urban canyon environments, then continuous operation is maintained, but performance degrades due to signal blockage, attenuation, and multipath
Solution Approach 1:
The patent merges GNSS-based navigation data with IMU-based navigation data through a tightly-coupled integration filter. The filter combines the long-term accuracy of GNSS with the short-term responsiveness and independence of IMU, creating a hybrid navigation system that maintains reliability in GNSS-denied environments. The integration filter processes both data sources simultaneously, weighting them according to their respective quality and availability.
Solution Approach 2:
The integration filter acts as an intermediary between GNSS and IMU systems, mediating the combination of their measurements. It processes GNSS data when available and IMU data when GNSS is unavailable, smoothing transitions and reconciling differences between the two systems. This intermediary function enables seamless operation across varying signal conditions, maintaining navigation reliability without requiring direct GNSS coverage.
3Measurement precision
If tightly-coupled integration filter is implemented, then navigation accuracy is improved, but device complexity increases
Solution Approach 1:
The integration filter is designed to be self-calibrating and self-adjusting, automatically adapting to changing operational conditions without requiring external intervention. The filter monitors signal quality, adjusts its internal parameters, and performs real-time bias compensation autonomously. This self-service capability reduces the need for complex manual calibration procedures and configuration, thereby managing system complexity while maintaining high navigation accuracy.
Data Source
AI summary
Embodiments of the invention provide methods of calibrating a blending filter based on extended Kalman filter (EKF), which optimally integrates the IMU navigation data with all other satellite measurements (tightly-coupled integration filter). In one embodiment a coordinate transformation matrix using a latest position fix is created. The state variables (for user velocity) are transformed to a local navigation coordinate. The state variables of said integration filter is estimated. A blended calibrated position fix is the output of the method.