Inertial Navigation Kalman Filter Zero-Velocity Update
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Solution Overview
Problem
Human-portable inertial navigation systems face challenges in maintaining accurate real-time position estimation over an extended period, especially in environments with magnetic disturbances and limited access to GPS, due to inherent errors in inertial measurement units and magnetic compass data.
Innovation Solution
The method involves a data processing system that uses a Kalman filter to perform zero-velocity updates and fixed-azimuth updates, combined with aiding measurements from GPS, RFID tags, and magnetic field filtering to constrain navigation errors, ensuring accurate position estimation even in challenging environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If inertial measurement units are used for navigation, then position estimation capability is improved, but navigation errors accumulate over time
Solution Approach 1:
The patent implements a feedback mechanism by detecting zero-velocity conditions and using them to correct accumulated navigation errors. The system continuously monitors IMU data, identifies when the device is stationary, and applies corrections at these moments, creating a closed-loop error correction system that prevents unbounded error growth over time.
Solution Approach 2:
The system performs preliminary error correction by detecting zero-velocity conditions before significant navigation errors accumulate. By proactively identifying stationary periods and applying corrections at these predetermined moments, the system prevents error accumulation rather than reacting to it after it has grown large.
2Weight of moving object
If tactical-grade or industrial-grade IMUs are used instead of navigation-grade IMUs, then weight and cost are reduced, but position estimation accuracy decreases
Solution Approach 1:
The patent enables the navigation system to self-correct its own errors by utilizing zero-velocity detection and fixed-azimuth update mechanisms. The system uses its own sensor data to identify correction opportunities and automatically applies corrections without external intervention, allowing lower-grade IMUs to achieve navigation-grade accuracy through self-correcting algorithms.
Solution Approach 2:
The system dynamically changes operational parameters by switching between different update modes (zero-velocity updates, fixed-azimuth updates, and standard IMU integration) based on detected conditions. This adaptive parameter adjustment allows the system to optimize accuracy for the current operational state, compensating for the lower inherent accuracy of tactical or industrial-grade IMUs.
3Adaptability or versatility
If magnetic field data is used for heading estimation, then navigation capability is improved, but magnetic disturbances cause errors
Solution Approach 1:
The patent extracts and removes the harmful magnetic disturbance component from the navigation solution by detecting fixed-azimuth conditions where the device is stationary and the heading should not change. During these periods, the system isolates and corrects magnetic errors by comparing expected versus actual heading, effectively extracting the disturbance from the navigation calculation.
Solution Approach 2:
The system converts the harmful effect of magnetic disturbances into a beneficial correction opportunity. By detecting when the device is stationary (fixed azimuth), the system uses these same conditions that reveal magnetic errors to apply corrections. The previously harmful magnetic interference becomes the trigger for error correction, turning the problem into a solution.
4Measurement precision
If GPS is used for position correction, then accuracy is improved, but GPS availability is limited in certain environments
Solution Approach 1:
The patent uses inexpensive, always-available zero-velocity and fixed-azimuth detection mechanisms as substitutes for expensive, environment-dependent GPS systems. These disposable correction opportunities occur frequently in urban canyons and indoor environments where GPS is unavailable, providing continuous error correction without requiring external satellite signals.
Data Source
AI summary
A method for processing data in an inertial navigation system having a Kalman filter and computer-readable storage medium containing instructions to configure a processor to perform the same. The method produces more accurate estimates of the position, velocity and attitude of the inertial measurement unit. The method is fully automatic and enables zero-velocity updates and fixed-azimuth updates to be performed simultaneously. The method may also include a multi-stage filtering process to filter digital compass data when used in an environment with extraterrestrial magnetic field sources. The method may also include a fixed-lag smoother to improve estimates of the position, velocity and attitude of the inertial measurement unit. The method also may include processes to constrain altitude errors.