Magnetometer Calibration via Monte Carlo Modal Partitioning
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Solution Overview
Problem
Magnetometer systems face challenges in maintaining accurate calibration in changing magnetic environments due to external distortions, leading to erroneous sensor readings.
Innovation Solution
A system that continuously calibrates magnetometers using a Monte Carlo probability-based best fit determination combined with modal partitioning, processing multiple magnetic measurements over time to generate and apply new calibration parameters when predetermined criteria are met.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If magnetometers are calibrated for an original magnetic environment, then measurement precision is improved, but adaptability to changing magnetic environments deteriorates
Solution Approach 1:
The patent implements dynamic calibration by continuously monitoring magnetic field vectors and automatically updating calibration parameters when environmental changes are detected. The system transitions from static calibration to dynamic adaptation by processing sequences of magnetic vectors and computing updated calibration spheres when modal partitioning detects environmental changes, allowing the magnetometer to maintain precision across varying magnetic environments.
Solution Approach 2:
The system employs feedback mechanisms by continuously comparing incoming magnetic vectors against the current calibration model and using Monte Carlo probability-based best fit determination to assess calibration quality. When the system detects that measurement quality deteriorates beyond threshold values, it triggers recalibration sequences, creating a closed-loop feedback system that maintains optimal performance.
2Adaptability or versatility
If continuous calibration is performed to maintain accuracy in changing environments, then adaptability is improved, but device complexity increases
Solution Approach 1:
The calibration process is segmented into distinct operational modes: initial calibration mode, continuous monitoring mode, and recalibration mode. The system divides magnetic vector processing into sequences and uses modal partitioning to identify when environmental changes occur. This segmentation allows the system to perform complex calibration operations only when necessary, reducing overall computational burden.
Solution Approach 2:
Instead of performing full recalibration continuously, the system applies partial calibration actions only when modal partitioning detects environmental changes. The Monte Carlo probability-based best fit determination is applied selectively to assess whether recalibration is needed, performing computation only to the extent necessary to maintain accuracy without unnecessary overhead.
3Measurement precision
If manual recalibration is performed to correct magnetic distortions, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The system performs self-calibration by automatically detecting environmental changes through modal partitioning and executing recalibration sequences without user intervention. The processing element autonomously monitors magnetic vector sequences, determines when calibration drift occurs, and applies corrective calibration parameters, eliminating the need for manual recalibration operations.
Solution Approach 2:
The system performs preliminary calibration actions by maintaining a database of calibration parameters and pre-computing correction strategies. When environmental changes are detected, the system applies pre-prepared calibration corrections from the database rather than performing complete recalibration from scratch, significantly reducing the time required to restore measurement precision.
Data Source
AI summary
A system, a method, and a computer program product are provided. At predetermined times, a processing element processes a new vector represented by a last received magnetic measurement from a magnetometer and indicating a direction and a magnitude of a sensed magnetic field. When the new vector satisfies multiple criteria, the new vector is added to a determined vector set. The processing element processes multiple magnetometer measurements using a Monte Carlo best fit determination and modal partitioning to produce new calibration parameters that define a calibration sphere. The new calibration parameters are applied and stored in a memory element when at least a predetermined number of vectors are consecutively added to the determined one of the multiple vector sets.