Magnetism Data Generation Using Computational Network Correction
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Solution Overview
Problem
Measurement units, particularly in navigational devices, face inaccuracies and unreliability due to the high dependency on magnetometers, which are prone to errors from magnetic bias and external disturbances, leading to incorrect navigation operations.
Innovation Solution
A machine-learning based computational network generates accurate magnetism data by determining the movement of Earth's magnetic poles and magnetic field deviations, reducing the need for high-grade magnetometers and improving sensor fusion with accelerometers and gyroscopes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high-grade magnetometers are used to improve measurement accuracy, then measurement precision is improved, but device cost increases
Solution Approach 1:
The patent introduces a computational network as an intermediary that processes magnetometer readings to correct errors. Instead of relying solely on expensive high-grade magnetometers, the system uses a low-grade magnetometer combined with a computational network that applies correction algorithms to achieve accuracy comparable to high-grade magnetometers, thereby reducing device cost while maintaining measurement precision
Solution Approach 2:
The patent replaces the reliance on high-grade physical magnetometer hardware with a computational approach. The computational network substitutes for the need for expensive precision hardware by using software-based error correction, sensor fusion algorithms, and calibration techniques to compensate for low-grade magnetometer deficiencies
2Measurement precision
If magnetometers are used to improve orientation measurement, then measurement precision is improved, but reliability deteriorates due to magnetic bias and external disturbances
Solution Approach 1:
The patent implements feedback mechanisms where the computational network continuously monitors magnetometer readings, identifies errors caused by magnetic bias and external disturbances, and applies real-time corrections. The system uses feedback from multiple sensors (accelerometers, gyroscopes) to validate and correct magnetometer data, thereby maintaining reliable orientation measurements despite environmental interference
Solution Approach 2:
The computational network acts as an intermediary layer between the magnetometer and the navigation system. It filters out unreliable magnetometer readings caused by magnetic bias and external disturbances, and provides corrected orientation data to the navigation operations, thereby improving measurement reliability
3Measurement precision
If sensor fusion is performed to improve measurement accuracy, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The computational network performs multiple functions within a single integrated system: it calibrates sensors, fuses data from multiple sources, corrects errors, and generates navigation solutions. This multi-functional approach consolidates what would otherwise require separate complex subsystems, thereby improving measurement accuracy while managing device complexity
Data Source
AI summary
A system for generating magnetism data for a measurement unit is disclosed. The system is configured to obtain a set of measurement unit attributes and location information associated with the measurement unit. The set of measurement unit attributes may comprise a first magnetism data for the measurement unit. The system is configured to identify a plurality of reference magnetism sources in proximity of the measurement unit, based on the location information, and obtain reference magnetism data from the plurality of reference magnetism sources. The system is configured to generate second magnetism data for the measurement unit, based on the first magnetism data, the reference magnetism data, and a trained machine-learning based computational network.