Magnetometer Anomaly Detection for Motion Tracking
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Solution Overview
Problem
Magnetometer data in motion tracking systems is often corrupted by magnetic disturbances, leading to degraded accuracy in motion tracking, particularly in devices relying on batteries or embedded processing, where power and computational efficiency are crucial.
Innovation Solution
A method and system for detecting magnetic disturbances using multiple algorithms that compare magnetometer sensor data to reference values derived from calibration routines and location information, allowing for disturbance handling through data discard, reduced confidence indexing, or calibration procedures, ensuring efficient detection and handling of anomalies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If magnetometer data is used in motion tracking systems, then heading information accuracy is improved, but reliability deteriorates due to magnetic disturbances from ferrous objects and electric fields
Solution Approach 1:
The system performs preliminary calibration to establish reference magnitude and dip angle values before normal operation. During operation, current measurements are compared against these pre-established references to detect disturbances, allowing the system to proactively identify corrupted data before it significantly degrades tracking accuracy.
Solution Approach 2:
The system continuously monitors magnetometer data against reference values and provides feedback through disturbance detection. When disturbances are detected, the system adjusts its operation by discarding corrupted data or triggering recalibration, creating a closed-loop feedback mechanism that maintains reliability while preserving measurement precision when conditions are favorable.
2Reliability
If multiple disturbance detection algorithms are performed in parallel, then reliability is improved through better anomaly detection, but use of energy and computational resources increases
Solution Approach 1:
The disturbance detection system is segmented into multiple independent algorithms that can be selectively executed. Each algorithm focuses on specific aspects of disturbance detection (magnitude comparison, dip angle comparison, yaw angle comparison), allowing the system to distribute computational load and energy consumption across modular components rather than requiring all algorithms to run simultaneously at full intensity.
3Reliability
If magnetometer data is discarded during disturbances, then reliability is improved by avoiding corrupted measurements, but measurement precision deteriorates due to loss of heading information
Solution Approach 1:
Instead of simply discarding magnetometer data during disturbances, the system dynamically adjusts the confidence index parameter assigned to the data. This allows the system to maintain data continuity while adjusting the weight or reliability metric based on disturbance severity, preserving heading information when possible while protecting against corrupted measurements when necessary.
Data Source
AI summary
Systems and methods are disclosed for detecting when a magnetic anomaly may impact the quality of data being output by a magnetometer. A plurality of detection algorithms may be performed in parallel on the sensor data. Further, indication of a anomaly from one or a combination of the detection algorithms may cause the magnetometer data to have a reduced contribution in any sensor fusion operation or may be omitted from a sensor fusion operation as desired.