Magnetometer Bias Estimation Using Clustering Techniques
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Solution Overview
Problem
Existing magnetometer calibration techniques are time-consuming and require frequent recalibration due to varying local interference fields, especially when the user moves between different magnetic environments, leading to inaccuracies in determining the true north direction.
Innovation Solution
A computer-implemented method that selects a cluster of bias offsets from previously-calibrated readings, uses a representative bias offset and magnitude threshold to estimate the external magnetic field, and determines if the estimated field's magnitude is within a defined range, allowing for more efficient and accurate magnetometer calibration without the need for frequent recalibration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional calibration procedures are used to determine magnetometer bias offsets, then measurement precision is improved, but loss of time increases due to frequent recalibration requirements
Solution Approach 1:
The system performs preliminary clustering of bias offset data into multiple clusters representing different magnetic environments during initial calibration. This pre-organized structure allows the magnetometer to quickly select appropriate calibration data based on current environmental conditions without performing full recalibration, thus maintaining measurement precision while reducing recalibration time.
Solution Approach 2:
The system dynamically selects between multiple pre-clustered bias offset clusters based on real-time magnetic field conditions. Instead of using a static single calibration set, the system adapts by choosing the most appropriate cluster for current environmental conditions, maintaining accuracy across varying magnetic environments without requiring frequent manual recalibration.
2Device complexity
If static threshold matching is used for calibration data lookup, then device complexity is reduced, but measurement precision deteriorates due to gaps in calibration data coverage
Solution Approach 1:
The calibration data is segmented into multiple clusters based on magnetic field characteristics. Each cluster represents a specific magnetic environment and contains relevant bias offset data. This segmentation allows the system to match current conditions to the most appropriate cluster, improving calibration accuracy without requiring complex continuous data structures.
Solution Approach 2:
The system uses magnetic field magnitude as a dynamic parameter to select between different calibrated clusters. By changing the selection parameter from static threshold matching to dynamic magnitude-based cluster selection, the system achieves better coverage of varying magnetic environments while maintaining relatively simple algorithmic complexity.
Data Source
AI summary
In some implementations, a computer-implemented method includes receiving a reading from a magnetometer of a mobile device. A cluster from a plurality of clusters of bias offsets generated from previously-calibrated readings is selected. The selected cluster has a representative bias offset, a mean of magnitudes in the selected cluster, and a magnitude threshold. An external magnetic field is estimated based on the reading and the representative bias offset for the selected cluster. Whether a magnitude of the estimated external field is within a magnitude range defined by the mean magnitude and the mean magnitude plus the magnitude threshold is determined.