Quartz Flexible Accelerometer Calibration via Linear Regression
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Solution Overview
Problem
The existing methods for calibrating quartz flexible accelerometers in inertial navigation systems require frequent and lengthy testing on dedicated devices, leading to high costs due to the need for multiple devices and extensive calibration periods.
Innovation Solution
A method involving obtaining original data at multiple time nodes, establishing a linear regression model, and using it to forward predict performance parameters, reducing the need for frequent dedicated device testing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If calibration tests are performed frequently on dedicated devices, then measurement precision is improved, but device complexity and cost increase
Solution Approach 1:
The patent creates a virtual copy of the calibration testing process through linear regression modeling. Instead of physically testing on dedicated devices multiple times, the system builds a mathematical model that replicates the calibration behavior, allowing virtual calibration tests to replace physical ones while maintaining measurement precision
Solution Approach 2:
The patent replaces the mechanical calibration testing system with a computational linear regression model. The physical calibration process involving dedicated devices is substituted with mathematical calculations that predict performance parameters, eliminating the need for complex physical testing infrastructure
2Reliability
If calibration tests are performed over extended periods, then reliability is improved, but loss of time increases
Solution Approach 1:
The patent performs preliminary calibration testing to collect data, then uses this data to build a linear regression model that can predict future performance. This preliminary action allows the system to establish reliable prediction patterns without requiring continuous extended testing, significantly reducing the time needed for ongoing calibration while maintaining reliability
Solution Approach 2:
The patent creates a temporal copy of calibration results through prediction modeling. Instead of waiting for extended physical testing periods to verify reliability, the linear regression model generates predicted values that replicate the reliability assurance that would otherwise require months of continuous testing
3Measurement precision
If multiple dedicated devices are used for calibration, then measurement precision is improved, but cost increases
Solution Approach 1:
The patent makes the linear regression model a universal tool that can predict performance parameters across multiple time points and conditions without requiring separate dedicated devices for each test. The single mathematical model serves multiple calibration functions that would otherwise require multiple physical devices
Solution Approach 2:
The patent uses virtual copying through the prediction model to replace multiple physical dedicated devices. The linear regression model generates predicted calibration values that replicate what would be obtained from multiple actual devices, eliminating the need to purchase and maintain expensive dedicated calibration equipment
Data Source
AI summary
The invention discloses a method for improving the calibration efficiency of an accelerometer, comprising the following steps: S100: obtaining original data of the quartz flexible accelerometer at a plurality of time nodes; S200: establishing a linear regression model according to the original data; and S300: forward predicting predicted values of performance parameters of the quartz flexible accelerometer at a plurality of target time nodes according to the linear regression model. In the solutions of the application, the establishment of the linear regression model improves the calibration efficiency of the quartz flexible accelerometer, and reduces the number of times a dedicated device is used, thus saving costs.