Resonant Pressure Sensor Polynomial Calibration
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Solution Overview
Problem
Existing pressure sensor calibration methods using high-order polynomials are computationally burdensome, prone to oscillatory characteristics, and exhibit poor estimation at data boundaries, requiring double precision floating point operations that increase computational load and power consumption.
Innovation Solution
The method employs simple interpolating polynomial functions for one-dimensional representation, generating sets of polynomials for two-dimensional interpolation, using Newton's method for polynomial calculation and Lagrange's method for evaluation, reducing computational requirements and avoiding double precision calculations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high-order polynomials are used for calibration, then measurement precision is improved, but computational complexity increases significantly
Solution Approach 1:
The patent divides the two-dimensional calibration surface into multiple one-dimensional polynomial curves along different axes. Instead of using a single high-order polynomial in two variables, the calibration data is segmented into separate polynomial representations for each dimension, which are then combined through iterative substitution. This segmentation reduces the computational complexity while maintaining calibration accuracy.
Solution Approach 2:
The patent transforms a two-dimensional polynomial approximation problem into a series of one-dimensional polynomial problems. By representing the calibration surface as intersecting one-dimensional curves and using iterative substitution, the method reduces the dimensionality of each individual polynomial calculation while achieving the same overall calibration accuracy through the combination of multiple lower-dimensional polynomials.
2Measurement precision
If high-order polynomials are used for calibration, then measurement precision is improved, but calculation speed decreases
Solution Approach 1:
The calibration calculation is segmented into multiple steps involving lower-order polynomials rather than a single high-order polynomial calculation. This segmentation allows each individual polynomial evaluation to be computationally lighter and faster, while the overall accuracy is maintained through the iterative combination of these lower-order results.
Solution Approach 2:
By converting the two-dimensional high-order polynomial problem into sequential one-dimensional polynomial evaluations, the patent achieves faster calculation speed. Each one-dimensional polynomial requires fewer computational operations than an equivalent two-dimensional high-order polynomial, and the sequential evaluation approach optimizes the overall calculation efficiency.
3Measurement precision
If double precision floating point operations are used, then measurement precision is improved, but power consumption increases
Solution Approach 1:
The patent employs single precision floating point operations instead of double precision, using a computationally cheaper and energy-efficient approach. While single precision has slightly lower precision than double precision, the patent compensates for this by using carefully designed lower-order polynomials and iterative substitution methods that maintain sufficient accuracy for the application, thereby reducing power consumption.
Data Source
AI summary
A pressure sensor and method wherein a resonant pressure transducer has a frequency F which changed depending on the applied pressure P and the temperature T. A temperature sensor measures the temperature T. A memory includes stored therein data points corresponding to the sensor's frequency F at difference applied pressures and temperatures. A processor is configured to generate several polynomials from the data points and calculate Pμ at Tμ when the transducer frequency is Fμ.