Procedure Proposal for Minimising the Dynamic Error of Second-Order Sensors
Krzysztof Tomczyk1, Małgorzata Kowalczyk2, Ksenia Ostrowska2
1Faculty of Electrical and Computer Engineering, Cracow University of Technology, Warszawska 24, 31-155 Krakow, Poland.
Sensors (Basel, Switzerland)
|March 10, 2022
Summary
This study presents a method to minimize dynamic error in sensors using Monte Carlo (MC) modeling and integral-square criterion (ISC) optimization. The procedure enhances signal processing accuracy for various sensor applications.
Area of Science:
- Measurement Science and Instrumentation
- Signal Processing
- Computational Modeling
Background:
- Dynamic errors in sensors limit measurement accuracy.
- Accurate sensor modeling is crucial for effective error minimization.
- Existing methods may not sufficiently address dynamic error complexities.
Purpose of the Study:
- To propose and detail a procedure for minimizing dynamic error in sensors.
- To provide a framework for implementing this procedure using computational tools.
- To enhance the accuracy of sensor signal processing.
Main Methods:
- Sensor modeling via the Monte Carlo (MC) method.
- Determining maximum dynamic error using the integral-square criterion (ISC).
- Optimizing sensor model parameters by minimizing the ISC.
Main Results:
- A validated procedure for dynamic error minimization was developed.
- The method was successfully implemented in the frequency domain using MathCad 15.
- Validation was confirmed using a TMS320C6713 digital signal processor.
Conclusions:
- The proposed procedure effectively minimizes dynamic error in sensors.
- This approach enhances the accuracy of sensor output signals.
- The method is applicable to a wide range of measurement scenarios.
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