Magnetic-Aided IMU Heading Estimation
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Solution Overview
Problem
Inertial measurement units (IMUs) and attitude and heading reference systems (AHRS) face accuracy degradation due to noise-related errors, particularly gyro drift, in dynamic environments where gravitational forces are unobservable, leading to corrupted roll and pitch estimation.
Innovation Solution
A magnetic-aided prefilter process that computes a body-sensed navigational frame magnetic field vector, calculates attitude residuals, and produces a measurement-noise covariance matrix for Kalman filtering, along with anomaly filtering and intermittent gyro calibration to enhance accuracy and robustness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If accelerometer data is used to estimate roll and pitch in dynamic environments, then the system can provide attitude information, but the estimation becomes corrupted by non-gravitational forces leading to significant accuracy degradation
Solution Approach 1:
The patent extracts and removes the corrupted accelerometer-based roll and pitch estimates from the system. Instead of using accelerometer data in dynamic environments, the system relies solely on magnetometer data for attitude estimation, effectively taking out the harmful acceleration measurements from the calculation chain while preserving the ability to provide attitude information through alternative means.
Solution Approach 2:
The patent introduces magnetometer data as an intermediary to bridge the gap between inertial measurements and accurate attitude estimation. The magnetometer serves as a mediator that provides reference information about the horizontal frame, allowing the system to calculate accurate roll and pitch without relying on corrupted accelerometer data during dynamic motion.
2Measurement precision
If magnetic field data is used to correct heading drift, then accuracy can be maintained, but the system becomes vulnerable to magnetic anomalies that corrupt the magnetic measurements
Solution Approach 1:
The patent applies dynamic weighting to magnetic field measurements based on their reliability. The system continuously assesses the quality of magnetometer data and adjusts the weight given to magnetic corrections in real-time. When magnetic anomalies are detected or suspected, the weighting dynamically reduces the influence of magnetic data, preventing corruption while maintaining accuracy during normal conditions.
Solution Approach 2:
The patent changes the parameter of measurement reliability assessment by introducing a quality metric for magnetic field data. This parameter change allows the system to distinguish between reliable and unreliable magnetic measurements, enabling selective use of magnetic corrections based on the current environmental conditions and anomaly presence.
3Reliability
If Kalman filtering is used to dynamically estimate parameters during motion, then real-time calibration is achieved, but the system complexity increases
Solution Approach 1:
The patent performs preliminary calibration during static periods before dynamic operation begins. By completing initial calibration when the system is stationary and magnetic field conditions are stable, the system reduces the computational burden during dynamic operation. The preliminary action of calibration stores reference values that simplify real-time processing during motion, maintaining reliability while reducing ongoing complexity.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The solution provides accurate onboard estimation of heading, pitch, and roll in dynamic environments without relying on accelerometer data, effectively mitigating gyro drift and other inertial measurement errors, and compensating for magnetic anomalies, thus maintaining accuracy over a wide geographical area.
Implementation Method 1
Some IMUs contain an accelerometer triad, a gyro triad and a magnetometer triad. In these devices the magnetic field measured by the magnetometers is blended with the data from the gyros and accelerometers
Implementation Method 2
The first group is an accelerometer triad. An accelerometer triad measures translational values using three sensors—each of which generates a separate analog signal representing a raw measured acceleration in one of the three dimensions
Implementation Method 3
The second group of sensors includes three angular rate sensors (gyros) which each measure the angular velocity (rate) in one of the three dimensions
Data Source
AI summary
Disclosed is a system and method for onboard optimal estimation of heading, pitch, and roll through real-time measurement of magnetic field, acceleration and angular motion in three dimensions. Magnetometer information is used to create an initial reference from which movement is measured. Thus, the process does not have to start when the body is in a known position. Further, the device does not have to continually rely on accelerometer data to get roll and pitch. To do this magnetic field data is used to complement gyro information. The magnetic data is used to estimate pitch, roll, and heading.