Magnetometer Calibration via Anomaly Detection and Ellipsoidal Fitting
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Magnetometers in portable electronic devices face challenges in achieving high accuracy due to factors like hard iron, soft iron, and magnetic anomalies, requiring precise calibration methods to determine device orientation with respect to magnetic North.
Innovation Solution
A method for calibrating magnetometers that automatically computes calibration quality, detects anomalies, and uses ellipsoidal fitting to isolate errors, generating calibration parameters based on user motion data and sanity checks, ensuring accurate and efficient calibration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional calibration methods are used, then the calibration process is simple, but the accuracy and stability of magnetometer measurements are insufficient due to hard iron effect, soft iron effect, and magnetic anomalies
Solution Approach 1:
The calibration process is segmented into multiple distinct phases: data collection phase, quality assessment phase (with anomaly detection), and calibration parameter computation phase. This segmentation allows each phase to be optimized independently, improving overall accuracy while managing complexity through structured progression
Solution Approach 2:
The system performs preliminary anomaly detection and data quality assessment before computing calibration parameters. By detecting magnetic anomalies and assessing data quality in advance, the system prevents incorrect calibration computations, thereby improving accuracy without requiring complex real-time correction mechanisms
Solution Approach 3:
The system implements feedback mechanisms by continuously monitoring calibration quality indicators and anomaly detection results. This feedback loop allows the system to assess whether collected data is suitable for calibration and to trigger re-calibration when necessary, improving measurement precision through iterative refinement
2Measurement precision
If comprehensive calibration data collection is performed to improve accuracy, then the calibration quality improves, but the time required for calibration increases
Solution Approach 1:
The system collects calibration data continuously in the background during normal device operation rather than requiring a dedicated calibration session. This partial action approach accumulates sufficient data over time without interrupting user workflow, achieving accurate calibration without significant time loss
Solution Approach 2:
The calibration process operates autonomously by automatically collecting data, detecting anomalies, assessing quality, and computing parameters without user intervention. The system serves itself by managing the entire calibration workflow, reducing both perceived time and user effort while maintaining high accuracy
3Reliability
If anomaly detection is implemented to improve calibration stability, then the reliability of calibration parameters improves, but the computational complexity and processing time increase
Solution Approach 1:
The system extracts and isolates anomaly detection as a separate, dedicated function within the calibration pipeline. By taking out the anomaly detection step and making it a distinct module with specific algorithms, the system improves reliability without propagating complexity throughout the entire calibration process
Solution Approach 2:
The anomaly detection mechanism acts as an intermediary between raw data collection and calibration parameter computation. This intermediary layer filters out unreliable data before it reaches the calibration algorithm, improving reliability while keeping the overall system manageable through clear separation of concerns
4Adaptability or versatility
If multiple sets of calibration parameters are stored and weighted, then the long-term accuracy and adaptability improve, but the memory requirements and processing overhead increase
Solution Approach 1:
The system changes the state of calibration parameters from static to dynamic by implementing time-based weighting and decay mechanisms. Older calibration parameters are automatically down-weighted over time, allowing the system to adapt to changing environmental conditions while managing memory usage through automatic parameter lifecycle management
Solution Approach 2:
The calibration parameter storage system is made dynamic with automatic weighting and expiration. Instead of storing all historical parameters with equal weight, the system dynamically adjusts the influence of each parameter based on recency and quality metrics, improving adaptability while controlling memory consumption through automatic pruning of obsolete data
Data Source
AI summary
A method includes acquiring magnetic data from a magnetometer, processing the magnetic data to perform robust calibration, and generating optimum calibration parameters using a calibration status indicator. To that end, the method includes generating a calibration status indicator as a function of time elapsed since a last calibration and variation in total magnetic field in previously stored magnetic data, detecting anomalies, and extracting a sparse magnetic data set using comparison between the previously stored magnetic data and the magnetic data. Calibration parameters are generated for the magnetometer using a calibration method as a function of the magnetic data set. The calibration parameters are stored based on performing a validation and stability check on the calibration parameters, and weighted with the previously stored calibration parameters to produce weighted calibration parameters. Calibration settings are generated as a function of the weighted calibration parameters, if the weighted calibration parameters were produced.