The Newton’s Polynomial Based - Automatic Model Generation (AMG) for Sensor Calibration to Improve the Performance of the Low-Cost Ultrasonic Range Finder (HC-SR04)

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Gutama Indra Gandha
Dewi Agustini Santoso


The ultrasonic range finder sensors is a general-purpose sensor to measure the distance contactless. This sensor categorized as low-cost sensor that widely used in various application. This sensor has a significant deviation that lead to significant error in the measurement result. The error that produced by this sensor tends to increase proportionally to the measured distance. The implementation of the particular algorithm is required to reduce the error value. The model-based calibration is a solution to increase the accuracy. The model-based solutions are no longer feasible if the states of the model have changed. The longer of the usage of the sensor lead to sensor fatigue. Sensor fatigue is one of the causes of model state changes. As long as the drift still within the tolerance limit, the performance of the sensor still can be restored by using calibration method. The model-based calibration calibrates the sensor by using the model. The update of the model must be made whenever the changing of the model state occurred. Since the manual model making process is not an easy task, time and cost required, then the Newton polynomial-based AMG (Automatic Model Generation) have been implemented to this research. The AMG algorithm generates the new sensor model automatically based on the most updated states. This automatic model generation is implemented in the calibration process of the ultrasonic sensor. The implementation of polynomial-based AMG algorithm for sensor calibration have been succeeded to improve the accuracy of the calibrated sensor by 96.4% and reduce the MSE level from 25.6 to 0.914.


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G. Gandha and D. A. Santoso, “The Newton’s Polynomial Based - Automatic Model Generation (AMG) for Sensor Calibration to Improve the Performance of the Low-Cost Ultrasonic Range Finder (HC-SR04)”, INFOTEL, vol. 12, no. 3, Aug. 2020.


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