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 is categorized as a low-cost sensor that is widely used in various applications. This sensor has a significant deviation that leads to significant errors in the measurement result. The error produced by this sensor tends to increase proportionally to the measured distance. The implementation of a particular algorithm is required to reduce the error value. The model-based calibration is a solution to increase accuracy. The model-based solutions are no longer feasible if the states of the model have changed. The length of the usage of the sensor leads to sensor fatigue. Sensor fatigue is one of the causes of model state changes. If the drift is still within the tolerance limit, the sensor performance can still be restored using the 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 (Automatic Model Generation (AMG) has been implemented in 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 a polynomial-based AMG algorithm for sensor calibration has been succeeded in improving the calibrated sensor’s accuracy by 96.4% and reducing 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, pp. 115-122, Aug. 2020.


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