Distance has been one of the basic factors in manufacturing and control fields, and ultrasonic distance sensors have been widely used as a low-cost measuring tool. However, the propagation of ultrasonic waves is greatly affected by environmental factors such as temperature, humidity and atmospheric pressure. In order to solve the problem of inaccurate measurement, which is significant within industry, this paper presents a novel ultrasonic distance sensor model using networked error correction (NEC) trained on experimental data. This is more accurate than other existing approaches because it uses information from indirect association with neighboring sensors, which has not been considered before. The NEC technique, focusing on optimization of the relationship of the topological structure of sensor arrays, is implemented for the compensation of erroneous measurements caused by the environment. We apply the maximum likelihood method to determine the optimal fusion data set and use a neighbor discovery algorithm to identify neighbor nodes at the top speed. Furthermore, we adopt the NEC optimization algorithm, which takes full advantage of the correlation coefficients for neighbor sensors. The experimental results demonstrate that the ranging errors of the NEC system are within 2.20%; furthermore, the mean absolute percentage error is reduced to 0.01% after three iterations of this method, which means that the proposed method performs extremely well. The optimized method of distance measurement we propose, with the capability of NEC, would bring a significant advantage for intelligent industrial automation.
References
[1]
Escol, A.; Planas, S.; Rosell, J.R.; Pomar, J.; Camp, F.; Solanelles, F.; Gracia, F.; Llorens, J.; Gil, E. Performance of an ultrasonic ranging sensor in apple tree canopies. Sensors 2011, 11, 2459–2477.
[2]
Andjar, D.; Weis, M.; Gerhards, R. An ultrasonic system for weed detection in cereal crops. Sensors 2012, 12, 17343–17357.
[3]
Majchrzak, J.; Michalski, M.; Wiczynski, G. Distance estimation with a long-range ultrasonic sensor system. IEEE Sens. J. 2009, 9, 767–773.
[4]
Giannoccaro, N.; Spedicato, L.; di Castri, C. A new strategy for spatial reconstruction of orthogonal planes using a rotating array of ultrasonic sensors. IEEE Sens. J. 2012, 12, 1307–1316.
[5]
Lee, J.R.; Chia, C.C.; Shin, H.J.; Park, C.Y.; Yoon, D.J. Laser ultrasonic propagation imaging method in the frequency domain based on wavelet transformation. Opt. Lasers Eng. 2011, 49, 167–175.
[6]
Wang, X.; Tang, Z. Optional optimization algorithms for time-of-flight system. IEEE Trans. Instrum. Meas. 2011, 60, 3326–3333.
[7]
Zhao, H.; Song, P.; Urban, M.W.; Kinnick, R.R.; Yin, M.; Greenleaf, J.F.; Chen, S. Bias observed in time-of-flight shear wave speed measurements using radiation force of a focused ultrasound beam. Ultrasound Med. Biol. 2011, 37, 1884–1892.
[8]
Canali, C.; de Cicco, G.; Morten, B.; Prudenziati, M.; Taroni, A. A temperature compensated ultrasonic sensor operating in air for distance and proximity measurements. IEEE Trans. Ind. Electron. 1982, IE-29, 336–341.
[9]
Carullo, A.; Ferraris, F.; Graziani, S.; Grimaldi, U.; Parvis, M. Ultrasonic distance sensor improvement using a two-level neural-network. IEEE Trans. Instrum. Meas. 1996, 45, 677–682.
[10]
Angrisani, L.; Baccigalupi, A.; Moriello, R.S.L. A measurement method based on Kalman filtering for ultrasonic time-of-flight estimation. IEEE Trans. Instrum. Meas. 2006, 55, 442–448.
[11]
Jiang, S.B.; Yang, C.M.; Huang, R.S.; Fang, C.Y.; Yeh, T.L. An innovative ultrasonic time-of-flight measurement method using peak time sequences of different frequencies: Part I. IEEE Trans. Instrum. Meas. 2011, 60, 735–744.
[12]
Hajiyev, C. Innovation approach based measurement error self-correction in dynamic systems. Measurement 2006, 39, 585–593.
[13]
Yan, T.H.; Wang, W.; Chen, X.D.; Li, Q.; Xu, C. Design of a smart ultrasonic transducer for interconnecting machine applications. Sensors 2009, 9, 4986–5000.
[14]
Phan, P.A.; Gale, T. Two-mode adaptive fuzzy control with approximation error estimator. IEEE Trans. Fuzzy Syst. 2007, 15, 943–955.
[15]
Gu, L.; Kou, X.; Jia, J. Distance Measurement for Tower Crane Obstacle Based on Multi-Ultrasonic Sensors. Proceedings of 2012 IEEE International Conference on Automation Science and Engineering (CASE), Seoul, Korea, 20–24 August 2012; pp. 1028–1032.
[16]
Tsai, T.M.; Yen, P.H. Improvement in stage measuring technique of the ultrasonic sensor gauge. Measurement 2012, 45, 1735–1741.
[17]
Srinivasan, S.; Pandharipande, A. Self-configuring scheduling protocol for ultrasonic sensor systems. IEEE Sens. J. 2013, 13, 2517–2518.
[18]
Kumar, R. Game theoretic pattern analysis for identification of odors/gases using response of a poorly selective sensor array. IEEE Sens. J. 2013, 13, 1110–1116.
[19]
Lee, K.Y.; Huang, C.F.; Huang, S.S.; Huang, K.N.; Young, M.S. A high-resolution ultrasonic distance measurement system using vernier caliper phase meter. IEEE Trans. Instrum. Meas. 2012, 61, 2924–2931.
[20]
Bischoff, O.; Heidmann, N.; Rust, J.; Paul, S. Design and implementation of an ultrasonic localization system for wireless sensor networks using angle-of-arrival and distance measurement. Proced. Eng. 2012, 47, 953–956.
[21]
Zhang, X.B.; Yang, B.C.; Zhang, T.; Zhang, S.Y. Local influence on the error-correction variable in a cointegrated system. J. Syst. Eng. Electron. 2001, 12, 1–8.
[22]
Singh, M.; Kumar, P. An efficient forward error correction scheme for wireless sensor network. Proced. Technol. 2012, 4, 737–742.
[23]
Luo, J.; Shi, C.; Shi, W.; Ling, Q. Joint Node Localization and Error Ccorrection in Wireless Sensor Networks. Proceedings of the 31st Chinese Control Conference (CCC), Anhui, China, 25–27 July 2012; pp. 6600–6604.
[24]
Yu, K.; Barac, F.; Gidlund, M.; Akerberg, J. Adaptive Forward Error Correction for Best Effort Wireless Sensor Networks. Proceedings of 2012 IEEE International Conference on Communications (ICC), Ottawa, Canada, 10–15 June 2012; pp. 7104–7109.
[25]
Barceló-Lladó, J.E.; Pérez, A.M.; Seco-Granados, G. Enhanced correlation estimators for distributed source coding in large wireless sensor networks. IEEE Sens. J. 2012, 12, 2799–2806.
[26]
Zhang, J.; Shen, X.; Zeng, H.; Dai, G.; Bo, C.; Chen, F.; Lv, C. Energy-efficient and localized lossy data aggregation in asynchronous sensor networks. Int. J. Commun. Syst. 2012, 26, 989–1010.
[27]
Malek, N.I.; Ijardar, S.P.; Master, Z.R.; Oswal, S.B. Temperature dependence of densities, speeds of sound, and derived properties of cyclohexylamine + cyclohexane or benzene in the temperature range (293.15 to 323.15 K). Thermochim. Acta. 2012, 547, 106–119.
[28]
Tang, J.; Liu, J. Variance of speed of sound and correlation with acoustic impedance in canine corneas. Ultrasound Med. Biol. 2011, 37, 1714–1721.
[29]
Saxena, R.; Bhan, R.; Aggrawal, A. A new discrete circuit for readout of resistive sensor arrays. Sens. Actuators A: Phys. 2009, 149, 93–99.
