Least Mean Square Estimator for Motor Frequency Measurement Based on Linear Hall Sensor

선형 홀센서 기반의 모터 회전속도 측정을 위한 평균 최소 자승 추정기

  • 최가형 (연세대학교 전기전자공학과) ;
  • 나원상 (연세대학교 전기전자공학과) ;
  • 곽기석 (연세대학교 전기전자공학과) ;
  • 윤태성 (창원대학교 전기공학과) ;
  • 박진배 (연세대학교 전기전자공학과)
  • Published : 2008.05.01


Motor frequency can be measured by a hall sensor. Among the many hall sensors, a linear type hall sensor is good at high accuracy frequency measuring problem. However, in general, this linear type hall sensor has DC offset which can vary along sensor's operating voltage change. Therefore, In motor frequency measurement problem using the linear hall sensor, it needs an estimator that can estimate frequency and DC offset simultaneously. In this paper, we propose the least mean square estimator to estimate motor frequency. To verify its performance, we compare the LMS estimator with a commercial analog tachometer. Experimental results shows the proposed LMS estimator works well in varying frequency and stationary DC offset.


Hall sensor;Least mean square;Least square;DC offset;Frequency estimation


  1. H. C. So, "A closed form frequency estimator for a noisy sinusoid," Proc. 45th IEEE Midwest Symp. on Circuits and Systems, Tulsa, Oklahoma, USA, vol. 2, pp. 160-163, Aug. 2002
  2. V. U. Reddy, B. Egardt and T. Kailath, "Least square type algorithm for adaptive implementation of Pisarenko's harmonic retrieval method," IEEE Trans. Acoust., Speech, Signal Processing, vol. 30, No. 6, pp. 399-405, June 1982 https://doi.org/10.1109/TASSP.1982.1163905
  3. P. A. Regalia, "An adaptive unit-norm filter with applications to Karhunen-Love transformation," IEEE Trans. Circuits and Systems, vol. 37, No. 5, pp. 646-649, May 1990 https://doi.org/10.1109/31.55009
  4. Miodrag D. Kusljevis, "A simple recursive algorithm for frequency estimation," IEEE Trans. Instrum. Meas.,vol 53, No. 1, pp. 335-340, April 2004 https://doi.org/10.1109/TIM.2003.822707
  5. Haykin Simon, Adaptive filter theory, Prentice Hall, 2002
  6. V. F. Pisarenko, "The retrieval of harmonics by linear prediction" Geophys. J. Roy. Astron. Soc., Vol. 33, pp. 347-366, 1973 https://doi.org/10.1111/j.1365-246X.1973.tb03424.x
  7. W. S. Ra , I. H. Whang, J.Y.Ahn and J.B.Park, "Recursive robust least squares estimator for time-varying linear systems with a noise corrupted measurement matrix" control Theory & Applications, IET vol 1, No. 1, pp.104-112, January 2007 https://doi.org/10.1049/iet-cta:20050331
  8. Steven M. Kay Modern Spectral Estimation : Theory and Application, Prentice Hall, 1988
  9. H. C. So. and P. C. Ching, "Adaptive algorithm for direct frequency estimation" IEE Proc.-Radar Sonar Navig,, vol.515, No 6, pp359-364, December 2004
  10. H. J. Lee,W. S. Ra, T. S. Yoon and J. B. Park, "Practical pinch detection algorithm for low-cost antipinch window control system", ICIT 2005, IEEE Int. Conf., pp995-1000, 2005
  11. H. W. Kim and S. K. Sul, "New Motor Speed Estimator Using Kalman Filter in Low-Speed Range", IEEE Trans. Industrial Electronics, Vol. 43, No. 4, pp. 498-504, 1996 https://doi.org/10.1109/41.510642
  12. Griffiths, L. J, "Rapid Measurement of Digital Instantaneous Frequency," IEEE Trans. Acoust. Speech Signal Process. pp.207-222 1975
  13. W. S. Ra and I. H. Whang, "Recursive Weighted Robust Least Squares Filter for Frequency Estimation", SICE-ICASE 2006, Int. Joint Conf., pp774-778, 2006
  14. P. A. Thompson, "An adaptive spectral analysis technique for unbiased frequency estimation in the presence of white noise," Proc. 13th Asilomar Conf. Circuits, Syst., comput., Pacific Grove, CA, pp.529-533, Nov. 1979