JACIII Vol.14 No.5 pp. 555-561
doi: 10.20965/jaciii.2010.p0555


Implementation of Passive Telemetry RF Sensor System Using Unscented Kalman Filter Algorithm

John-Tark Lee*, Kyung-Yeop Kim**, and Su-Ho Lee*

*Department of Electrical Engineering, Dong-A University, 840, Hadan 2 dong, Shaku, Busan 604-714, Korea

**Senior Researcher, R&D Center, KJ Radio Corporation, 2F, Dukchang Bldg., 8-1, 2-Ga, DongKwang-Dong, Jung-Gu, Busan 600-022, Korea

March 26, 2010
March 29, 2010
July 20, 2010
passive telemetry, inductive coupling, unscented Kalman filter, capacitive parameter, nonlinearity

This article describes a newly designed and implemented capacitive sensor system. An Unscented Kalman Filter (UKF) algorithm based passive telemetry RF sensor system was modeled with parasitic parameters over a range of high frequencies of approximately 200 KHz. And the system was also successfully implemented on the DSP. Under the constraints that it should be “wireless,” “implantable,” and “batteryless,” the system was simply built to consisted of passive components R, L and C by the inductive coupling principle, and it focused on the accurate estimation of a capacitive parameter in the secondary part. The UKF algorithm, which can coped with the drawbacks of the extended Kalman filter in noisy nonlinear systems, was applied to estimate the capacitive parameter with nonlinearity. The input/output learning data for the UKF algorithm were acquired from the specially designed phase difference detector and amplitude detector. The newly suggested parameter estimation technique can be easily applied to the precise measurement system, which should coped with sensitive environmental changes, such as changes in pressure or humidity.

Cite this article as:
John-Tark Lee, Kyung-Yeop Kim, and Su-Ho Lee, “Implementation of Passive Telemetry RF Sensor System Using Unscented Kalman Filter Algorithm,” J. Adv. Comput. Intell. Intell. Inform., Vol.14, No.5, pp. 555-561, 2010.
Data files:
  1. [1]
  2. [2] American National Standards Institute, “Safety Level of Electromagnetic Radiation with respect to Personnel,” ANSI C95.1-1974, IEEE, NewYork, NY, 1974.
  3. [3] Akar, T. Akin, and K. Najafi, “A Wireless batch sealed absolute Capacitive Pressure Sensor,” Sensor and Actuators, A 95, pp. 29-38, 2001.
  4. [4] T. J. Hapster, B. Stark, and K. Najafi, “A Passive Wireless Integrated Humidity Sensor,” The 14th IEEE Int. Conf. on MEMS, pp. 553-557, 2001.
  5. [5] T. Varpula and O. Jaakkola, “Low Cost Wireless RF Sensors,” Automation Technology Review, pp. 12-17, 2001.
  6. [6] K.-Y. Kim, J. T. Lee, D.-K. Yu, and Y.-S. Park, “Parameter Estimation of Noisy Passive Telemetry Sensor System Using UKF,” Future generation communication and networking, Vol.2, pp. 433-438, 2007.
  7. [7] K. Y. Kim and J. T. Lee, “Capacitive Parameter Estimation of Passive Telemetry RF Sensor System Using RLS Algorithm,” Trans. KIEE, Vol.57, No.5, pp.858-865, 2008.
  8. [8] K. J. Cho and H. Harry Asda, “A Recursive Frequency Tracking Method for Passive Telemetry Sensors,” d’Arbeloff Laboratory for Information Systems and Technology, 2003.
  9. [9] S. B. Park, “Circuit Theory,” MoonUnDang, second edition, 2000.
  10. [10] H. Haruta, “Impedence Measurement Manual,” Agilent Techology, Vol.2, 2001.
  11. [11]
  12. [12] S. Julier, J. Uhlmann, and H. F. Durrant-Whyte, “A new Method for the Nonlinear Transformation for Means and Covariances in Filters and Estimations,” IEEE Trans. on Automatic Control, Vol.45, pp. 477-482, March 2000.
  13. [13] S. J. Julier and J. K. Uhlmann, “A new Extension of the Kalman Filter to Nonlinear System,” in Proc. of AeroSence: The 11th Int. Symposium on Aerospace/Defence Sensing, simulation and controls, 1997.
  14. [14] E. A. Wan and R. van der Merwe, “The UKF,” in Kalman filtering and Neural Networks Edited by S.Haykin, John Wiley and Sons, Inc., 2001.
  15. [15] E. A. Wan and R. van er Merwe, “UKF for nonlinear estimation,” in Proc. of the IEEE 2000 Adaptive Systems for signal Proc., Communications and Control Symposium (CA-SPCC), pp. 153-158, 2000.
  16. [16] J. Ma, “Predict Chaotic Time-Series using UKF,” Proc. of the Third Int. Conf. on Machine Learning and Cybernetics, pp. 26-39, August 2004.
  17. [17] Haykin, “Kalman Filtering and Neural Networks,” John Wiley & Sons Inc, 2001.
  18. [18]
  19. [19] Malvino, “Electronic Principle,” McGROW-HILL, pp. 840-841, 1999.
  20. [20] S. Y. Nam, S. G. Byun, and G. I. Jung, “RFID Structure and Application using ED-310,” SangHakDang, 2006.
  21. [21] Hayt, “Engineering Electromagnetics,” McGRAW-HILL, fifth edition, 1989.
  22. [22] S. J. Julier, “The Scaled Unscented Transform,” Proc. of the American Control Conf., Anchorage, AK May, 2002.
  23. [23] B. C. Kuo, “Automatic Control,” John Wiley & Sons, Inc., 2003.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Mar. 05, 2021