Magnetic Position Sensing With Compact RNNs Under Disturbance Fields
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Solution Overview
Problem
Existing magnetic sensor systems face challenges in achieving high accuracy and robustness while being sensitive to external disturbances, temperature variations, and mounting errors, often requiring complex trade-offs between different performance criteria.
Innovation Solution
A method and system using a recurrent neural network (RNN) with a limited number of trainable parameters to determine the position of a sensor device relative to a magnetic source, utilizing a semiconductor substrate with magnetic sensors, which are less sensitive to external disturbances and temperature variations, and can operate with minimal sensor elements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional magnetic sensor systems are used to achieve high measurement accuracy, then position determination precision is improved, but sensitivity to external disturbance fields and temperature variations increases
Solution Approach 1:
The patent implements feedback mechanisms where sensor signals are processed through neural networks that continuously adjust position estimates based on observed magnetic field variations. The system uses feedback from multiple sensor elements to compensate for external disturbances and temperature effects, maintaining accurate position determination despite harmful environmental factors.
Solution Approach 2:
The patent changes operational parameters by using normalized sensor signals and adaptive processing thresholds. The neural network adjusts its processing parameters dynamically based on the magnitude of sensor inputs, allowing the system to maintain accuracy across varying environmental conditions without being overly sensitive to external disturbances or temperature changes.
2Measurement precision
If complex algorithms and multiple sensor elements are used to improve measurement accuracy, then position determination precision is improved, but device complexity increases
Solution Approach 1:
The patent segments the sensor array into multiple discrete sensor elements, each measuring local magnetic field characteristics. This segmentation allows the system to achieve high measurement precision through distributed sensing while keeping individual sensor elements simple and the overall architecture modular, reducing the complexity burden of requiring complex algorithms to process each sensor's output independently.
Solution Approach 2:
The patent uses multiple sensor elements that measure similar magnetic field quantities at different locations. These redundant measurements are processed through neural networks to improve accuracy without requiring complex algorithms, as the redundancy provides natural error correction and allows for simpler processing approaches.
3Measurement precision
If a large number of sensor elements are used to improve measurement accuracy, then position determination precision is improved, but manufacturing cost and device complexity increase
Solution Approach 1:
The patent applies local quality by positioning sensor elements at specific locations where they measure most informative magnetic field characteristics. Not all sensor elements are identical or positioned the same way - each is strategically placed to capture local field variations that contribute most to accurate position determination, improving precision while avoiding unnecessary sensor elements that would increase manufacturing cost.
Solution Approach 2:
The patent uses a partial set of sensor elements that provides sufficient measurement accuracy without requiring complete coverage. The neural network processing is designed to achieve good position determination with a reasonable number of sensors, avoiding excessive action by using more sensors than strictly necessary, thus controlling manufacturing cost while maintaining acceptable 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 achieves high positional accuracy with mean square errors below ±100 microns, even under non-ideal conditions, and operates with a simple neural network architecture, reducing sensitivity to external disturbances and temperature variations.
Implementation Method 1
They are based on measuring a magnetic field characteristic at one or multiple sensor locations
Data Source
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Figure 4A
AI summary
Method of determining a position (x; x, y) of a sensor device movable relative to a magnetic source, or vice versa; the sensor device comprising at least two magnetic sensors; the method comprising the steps of: a) obtaining a plurality of magnetic sensor signals from said magnetic sensors; b) determining or estimating the position of the sensor device based on said plurality of sensor signals or signals derived therefrom; wherein step b) comprises: determining said position (x; x, y) using an artificial neural network; the artificial neural network being a recurrent neural network trained for determining said position using at most three hundred (300) trainable parameters per degree of freedom. A position sensor system. A position sensor device.