Magnetic Sensor Calibration via 360 Spin and Spherical Fit
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Solution Overview
Problem
In GPS-denied environments, such as indoors or buildings, magnetic sensors face calibration errors and interference from local sources, leading to unreliable heading measurements, which hinders accurate navigation and tracking of personnel and assets.
Innovation Solution
A real-time calibration method for magnetic sensors involves spinning the sensor 360 degrees to capture data, fitting it to a sphere to account for z-direction offsets, and computing an indicator of magnetic heading reliability by comparing virtual and actual magnetic field vectors, while also using gyro and accelerometer data to filter out drift and interference.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If magnetic sensors are used for heading measurement in GPS-denied environments, then navigation capability is provided, but calibration errors and interference from local sources cause unreliable heading measurements
Solution Approach 1:
The patent introduces an intermediary calibration process that mediates between the magnetic sensor and the environment. By performing 360-degree spins and fitting spherical models to the collected data, the system creates a calibration model that filters out local interference sources. This intermediary calibration layer allows the sensor to operate reliably despite harmful environmental factors.
Solution Approach 2:
The patent changes the operational parameters of the magnetic sensor by rotating it through 360 degrees during calibration and processing the data in spherical coordinates. This parameter change from static to dynamic measurement, and from Cartesian to spherical coordinate system, enables the system to distinguish between Earth's magnetic field and local interference sources, thereby improving reliability.
2Measurement precision
If traditional circle-fitting calibration is used for magnetic sensors, then calibration is performed in the x-y plane, but z-direction offsets are not accounted for leading to reduced accuracy
Solution Approach 1:
The patent transitions from 2D circle-fitting calibration to 3D spherical calibration. By adding the z-dimension and fitting a sphere instead of a circle, the system accounts for all three spatial dimensions including z-direction offsets. This dimensional expansion improves measurement precision without significantly increasing operational complexity, as the spherical fit follows similar mathematical principles to circle fitting.
3Reliability
If magnetic sensor data is used directly for navigation, then simple processing is required, but calibration errors and drift accumulate over time reducing reliability
Solution Approach 1:
The patent performs preliminary calibration actions before navigation operations begin. By completing the 360-degree spin and spherical fitting calibration in advance, the system establishes accurate baseline parameters that prevent error accumulation during subsequent navigation. This preliminary action reduces the need for complex continuous correction algorithms during active navigation.
Solution Approach 2:
The patent implements feedback mechanisms where the calibrated magnetic heading measurements are continuously compared with other navigation data sources. This feedback loop allows the system to detect and correct drift accumulation over time, maintaining navigation reliability without requiring excessively complex processing algorithms.
Data Source
AI summary
Methods for calibrating a body-worn magnetic sensor by spinning the magnetic sensor 360 degrees to capture magnetic data; if the spin failed to produce a circle contained in an x-y plane fit a sphere to the captured data; determining offsets based on the center of the sphere; and removing the offsets that are in the z-direction. Computing a magnetic heading reliability of a magnetic sensor by determining an orientation of the sensor at one location; transforming the orientation between two reference frames; measuring a first vector associated with the magnetic field of Earth at the location; processing the first vector to generate a virtual vector when a second location is detected; measuring a second vector associated with the magnetic field of Earth at the second location; and calculating the magnetic heading reliability at the second location based on a comparison of the virtual vector and the second vector.