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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Abstract
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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[1]
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.
Section
Electronics
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References
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[12] Y. Luo, R. Wang, and L. Yao, “A Curve-fitting Calibration Method applied for Ultrasonic Flow-meter,” TELKOMNIKA Indones. J. Electr. Eng., vol. 11, no. 10, pp. 5669–5674, 2013, doi: 10.11591/telkomnika.v11i10.3392.
[13] M. Mozek, D. Vrtacnik, D. Resnik, U. Aljancic, M. Cvar, and S. Amon, “Calibration and error correction algorithms for smart pressure sensors,” 11th IEEE Mediterr. Electrotech. Conf. (IEEE Cat. No.02CH37379), pp. 240–243, 2002, doi: 10.1109/MELECON.2002.1014566.
[14] E. Köppe, D. Augustin, A. Liers, and J. Schiller, “Self-calibration-method for an inertial navigation system with three 3D sensors,” 1st IEEE Int. Symp. Inert. Sensors Syst. ISISS 2014 - Proc., pp. 3–6, 2014, doi: 10.1109/ISISS.2014.6782522.
[15] Y. Choi, Y. Bin Kim, and I. S. Jung, “A 100MS/s 10-bit Split-SAR ADC with Capacitor Mismatch Compensation Using Built-In Calibration,” Proc. - 2016 IEEE 25th North Atl. Test Work. NATW 2016, pp. 1–5, 2016, doi: 10.1109/NATW.2016.9.
[16] F. M. Schüffny, S. Höppner, A. Oefelein, and C. Mayr, “A multi-bit PFD architecture for ADPLLs with built-in jitter self-calibration,” Proc. - IEEE Int. Symp. Circuits Syst., vol. 2019-May, pp. 6–10, 2019, doi: 10.1109/ISCAS.2019.8702118.
[17] I. Sarkas, M. G. Girma, J. Hasch, T. Zwick, and S. P. Voinigescu, “A fundamental frequency 143-152 GHz radar transceiver with built-in calibration and self-test,” Tech. Dig. - IEEE Compd. Semicond. Integr. Circuit Symp. CSIC, pp. 5–8, 2012, doi: 10.1109/CSICS.2012.6340072.
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[19] H. Gao, Z. Wang, and L. Zhang, “Gyro online correctionmethod based on Kalman filter and polynomial fitting,” Proc. - 5th Int. Conf. Instrum. Meas. Comput. Commun. Control. IMCCC 2015, pp. 1144–1149, 2016, doi: 10.1109/IMCCC.2015.246.
[20] L. Zhang, R. L. Wang, and K. K. Liu, “Study on errors correction of infrared methane sensor based on Support Vector Machines,” 2009 2nd Int. Conf. Intell. Comput. Technol. Autom. ICICTA 2009, vol. 2, pp. 471–475, 2009, doi: 10.1109/ICICTA.2009.349.
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[24] S. Ogata and M. Kayama, “SML4C: Fully automatic classification of state machine models for model inspection in education,” Proc. - 2019 ACM/IEEE 22nd Int. Conf. Model Driven Eng. Lang. Syst. Companion, Model. 2019, pp. 720–729, 2019, doi: 10.1109/MODELS-C.2019.00109.
[25] Y. Liu and W. J. Ye, “Time consuming numerical model calibration using Genetic Algorithm (GA), 1-Nearest Neighbor (1NN) classifier and Principal Component Analysis (PCA),” Annu. Int. Conf. IEEE Eng. Med. Biol. - Proc., vol. 7 VOLS, pp. 1208–1211, 2005, doi: 10.1109/iembs.2005.1616641.
[26] H. Sansury, “The Annual Conference on Management and Information Technology ( ACMIT ) 2016 Ultrasonic Sonar Object and Range Detection Measurement Display using HC-SR04 Sensor on Arduino ATMEGA 2560,” no. February 2014, pp. 49–55, 2016.
[27] M. Andayani, W. Indrasari, and B. H. Iswanto, “Kalibrasi Sensor Ultrasonik Hc-Sr04 Sebagai Sensor Pendeteksi Jarak Pada Prototipe Sistem Peringatan Dini Bencana Banjir,” vol. V, pp. SNF2016-43-SNF2016-46, 2016, doi: 10.21009/0305020109.
[28] K. Bhatia and A. Pathak, “Factors affecting accuracy of distance measurement system based on ultrasonic sensor in air,” Int. J. Recent Technol. Eng., vol. 8, no. 2 Special Issue 11, pp. 2143–2144, 2019, doi: 10.35940/ijrte.B1222.0982S1119.
[29] W. Xie and P. Bai, “A pressure sensor calibration model based on support vector machine,” Proc. 2012 24th Chinese Control Decis. Conf. CCDC 2012, pp. 3239–3242, 2012, doi: 10.1109/CCDC.2012.6244512.
[30] Z. Mahmoudi, M. D. Johansen, J. S. Christiansen, and O. Hejlesen, “Comparison between one-point calibration and two-point calibration approaches in a continuous glucose monitoring algorithm,” J. Diabetes Sci. Technol., vol. 8, no. 4, pp. 709–719, 2014, doi: 10.1177/1932296814531356.
[31] G. Jiang and C. Zhao, “Camera calibration based on polynomial fitting,” 2010 Int. Conf. Comput. Intell. Softw. Eng. CiSE 2010, pp. 0–3, 2010, doi: 10.1109/CISE.2010.5677182.
[32] X. Yang, X. Meng, T. Jiang, and A. Husnain, “An error correction method based on polynomial fitting to improve the accuracy of the em indoor positioning system,” Proc. - 2016 6th Int. Conf. Instrum. Meas. Comput. Commun. Control. IMCCC 2016, no. 3, pp. 932–935, 2016, doi: 10.1109/IMCCC.2016.150.
[33] Z. Tang, P. Yan, and Luojun, “A novel ROV depth control based on LSM fitting predictor and fuzzy compensation,” ICACTE 2010 - 2010 3rd Int. Conf. Adv. Comput. Theory Eng. Proc., vol. 2, pp. 612–614, 2010, doi: 10.1109/ICACTE.2010.5579496.
[34] H. Shan, L. Yu, H. Wang, Y. Li, and B. Cao, “Calibration of Indoor EM Location System based on Polynomial Fitting,” Proc. 2019 IEEE 2nd Int. Conf. Electron. Inf. Commun. Technol. ICEICT 2019, pp. 873–876, 2019, doi: 10.1109/ICEICT.2019.8846317.
[35] L. Zhang, Y. Qin, and J. Zhang, “Study of polynomial curve fitting algorithm for outlier elimination,” 2011 Int. Conf. Comput. Sci. Serv. Syst. CSSS 2011 - Proc., pp. 760–762, 2011, doi: 10.1109/CSSS.2011.5974936.
[36] R. Srivastava and P. Srivastava, “Comparison of Lagrange’s and Newton’s interpolating polynomials,” J. Exp. Sci., vol. 3, no. 1, pp. 1–4, 2012, [Online]. Available: http://jexpsciences.com/index.php/jexp/article/viewArticle/12469.
[37] T. H. Feng, L. Wang, W. Zheng, S. Kanajan, and S. A. Seshia, “Automatic model generation for black box real-time systems,” Proc. -Design, Autom. Test Eur. DATE, pp. 930–935, 2007, doi: 10.1109/DATE.2007.364412.
[38] L. Zhang, Y. Cao, S. Wan, H. Kabir, and Q. J. Zhang, “Parallel automatic model generation technique for microwave modeling,” IEEE MTT-S Int. Microw. Symp. Dig., pp. 103–106, 2007, doi: 10.1109/MWSYM.2007.380265.
[39] F. De Dinechin, M. Joldes, and B. Pasca, “Automatic generation of polynomial-based hardware architectures for function evaluation,” Proc. Int. Conf. Appl. Syst. Archit. Process., pp. 216–222, 2010, doi: 10.1109/ASAP.2010.5540952.
[40] E. Holst and P. Thyregod, “A statistical test for the mean squared error,” J. Stat. Comput. Simul., vol. 63, no. 4, pp. 321–347, 1999, doi: 10.1080/00949659908811960.