Magnetic Sensor Clustering for Movable-Part Position Estimation
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Solution Overview
Problem
Existing estimation systems for devices with movable parts, such as rollable or foldable smartphones, face challenges in accurately determining the position and attitude of movable components due to disturbances in the magnetic field environment, which affect estimation accuracy.
Innovation Solution
An estimation apparatus that collects and clusters magnetic sensor data to separate temporal and continuous disturbances, updating reference information based on the most frequent data patterns to maintain accurate position and attitude estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If magnetic sensor data is collected continuously to improve estimation accuracy, then measurement precision is improved, but the system becomes more susceptible to temporal disturbances affecting reliability
Solution Approach 1:
The system performs preliminary clustering of magnetic sensor data to identify continuous disturbance patterns before using them for estimation. By pre-processing the data to separate temporal from continuous disturbances, the system establishes a reliable reference that is less susceptible to transient disturbances, thereby improving both measurement precision and reliability
Solution Approach 2:
The system uses clustering feedback to identify and separate continuous disturbance patterns from temporal variations in the magnetic sensor data. This feedback mechanism allows the system to update reference information with disturbance-separated data, improving estimation accuracy while maintaining reliability by accounting for continuous environmental factors
2Measurement precision
If reference information is updated frequently to maintain accuracy, then measurement precision is improved, but the system complexity increases
Solution Approach 1:
The system performs preliminary clustering analysis on magnetic sensor data to identify continuous disturbance patterns before updating reference information. This pre-processing step organizes the data into meaningful patterns, allowing for more efficient and accurate reference updates without requiring complex real-time processing algorithms
Solution Approach 2:
The system creates clustered representations of magnetic sensor data patterns that capture continuous disturbance characteristics. These clustered models serve as simplified copies of the complex raw data, enabling reference information updates based on pattern recognition rather than raw data processing, thereby reducing system complexity while maintaining precision
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system effectively calibrates reference information to improve estimation accuracy by distinguishing between transient and continuous disturbances, ensuring precise positioning and attitude determination of movable components.
Implementation Method 1
a magnetic sensor 20 configured to measure a magnetic field at a position in which the magnetic sensor 20 is provided, and output a measurement value in accordance with the magnetic field
Data Source
AI summary
An estimation apparatus comprises: a collection unit configured to collect a plurality of pieces of update information used for updating reference information, which indicates measurement values in accordance with a position or an attitude of a second portion against a first portion which is measured by at least one magnetic sensor while changing at least one of the position and the attitude of the second portion against the first portion over a predetermined movable range; a clustering unit configured to perform clustering on the plurality of pieces of update information according to a predetermined classification condition; and an updating unit configured to update the reference information based on at least one piece of update information included in a maximum class into which a highest number of pieces of information are classified among the plurality of pieces of update information.