Improving the response of a wheel speed sensor by using a RLS lattice algorithm

Sensors

Volumn 6, Issue 2, 2006, Pages 64-79

  Hernandez, Wilmar ^a  

^a UNIVERSIDAD POLITÉCNICA DE MADRID   (Spain)

Author keywords

Adaptive noise canceller; Recursive least squares lattice adaptive filter; Wheel speed sensor

Indexed keywords

ACCIDENT PREVENTION; ADAPTIVE FILTERS; AUTOMOTIVE INDUSTRY; POWERTRAINS; SIGNAL TO NOISE RATIO; SPEED; SPURIOUS SIGNAL NOISE; TRACTION CONTROL; WHEELS;

ADAPTIVE NOISE CANCELLER; ELECTRONIC STABILITY PROGRAMS; MAGNETIC RELUCTANCE; NOISE CHARACTERISTIC; OPTIMAL ESTIMATIONS; RECURSIVE LEAST SQUARE (RLS); TYPICAL APPLICATION; WHEEL SPEED SENSORS;

INDUCTIVE SENSORS;

EID: 33645154890     PISSN: 14243210     EISSN: 14248220     Source Type: Journal    
DOI: 10.3390/s6020064     Document Type: Article

References (58)

1. Microelectromechanical systems advanced materials and fabrication materials. National Academy Press, Washington, D.C., 1997.
2. Cooper, A. R.; Brignell, J. E. Electronic processing of transducer signals: Hall effect as an example. Sensors and Actuators A 1985, 7, 189-198.
3. Ko, W. H.; Fung, C. D. VLSI and intelligent transducers. Sensors and Actuators A 1982, 2, 239-250.
4. Middelhoek, S.; Hoogerwerf, A. C. Smart sensors: When and where?. Sensors and Actuators A 1985, 8, 39-48.
5. Giachino, J. M. Smart sensors. Sensors and Actuators A 1986, 10, 239-248.
6. Middelhoek, S.; French, P. J.; Huising, J. H.; Lian, W. J. Sensors with digital or frequency output. Sensors and Actuators A 1988, 15, 119-133.
7. Holzlein, K.; Larik, J. Silicon magnetic field sensor with frequency output. Sensors and Actuators A 1991, 26, 349-355.
8. Najafi, K. Smart sensors. Journal of Micromechanics and Microengineering 1991, 1, 86-102.
9. Juds, S. M. Toward a definition of smart sensors. Sensors 1991, 2-3.
10. Nihtianov, S.; Minkova, T. Magnetic-field-sensitive device with frequency output. Sensors and Actuators A 1992, 30, 101-104.
11. Ramsden, E. Measuring magnetic fields with fluxgate sensors. Sensors 1994, 87-90.
12. Hernandez, W. Fluxgate magnetometer for magnetic fields in the range 1-100uT. Electronics Letters 1995, 31, 2110-2111.
13. Rodriguez, F.; Trujillo, H.; Hernandez, W. A simple bandgap-type magnetoamplifier. Sensors and Actuators A 1996, 55, 133-137.
14. Hernandez, W. Magnetic-field sensor based on a relaxation oscillator. Sensors and Actuators A 1996, 55, 163-166.
15. Jacobsen, E. Signal conditioning a pressure sensor with a Filed-Programmable Analog Array. Sensors 1997, 81-86.
16. Frank, R. Understanding smart sensors, Second Edition; Artech House, Norwood, MA, 2000.
17. Hernandez, W. Improving the response of an accelerometer by using optimal filtering. Sensors and Actuators A 2001, 88, 198-208.
18. Hernandez, W. Improving the response of several accelerometers used in a car under performance tests by using Kalman filtering. Sensors 2001, 1, 38-52.
19. Hernandez, W. Improving the response of a wheel speed sensor using an adaptive line enhancer. Measurement 2003, 33, 229-240.
20. Hernandez, W. Improving the response of a wheel speed sensor by using frequency-domain adaptive filtering. IEEE Sensors Journal 2003, 3, 404-413.
21. Mörbe, M.; Zwiener, G. Wheel-speed sensors. In Sensors for Automotive Technology; Marek, J., Trah, H. -P., Suzuki, Y., Yokomori, I., Ed.; WILEY-VCH Verlag GmbH & Co. KgaA, Weinheim 2003, vol.4, 403-416.
22. Hernandez, W. Improving the response of wheel speed sensors by using robust and optimal signal processing techniques. IEEE International Symposium on Industrial Electronics, ISIE2005, Dubrovnik, Croatia, June 20-23, 2005, 1049-1053.
23. Hernandez, W. Robust multivariable estimation of the relevant information coming from a wheel speed sensor and an accelerometer embedded in a car under performance tests. Sensors 2005, 5, 488-508.
24. Hernandez, W. Improving the response of a rollover sensor placed in a car under performance tests by using a RLS lattice algorithm. Sensors 2005, 5, 613-632.
25. Hernandez, W. Wheel speed sensors. In Encyclopedia of Sensor; Grimes, C. A., Dickey, E. C., Pishko, M. V., Ed.; American Scientific Publishers, in press.
26. Gregory, B. A. Introduction to electrical instrumentation and measurement systems, Second Edition; The Macmillan Press Limited, England, 1981
27. Beckwith, T. G.; Buck, N. L.; Marangoni, R. D. Mechanical measurements, Third Edition; Addison-Wesley, Massachusetts, 1981
28. Norton, H. N. Sensor and analyzer handbook, Prentice-Hall, Englewood Cliffs, New Jersey, 1982.
29. Beckwith, T. G.; Marangoni, R. D.; Lienhard V, J. H. Mechanical measurements, Fifth Edition; Addison-Wesley, New York, 1993.
30. Carr, J. J. Sensors and circuits. Prentice-Hall, Upper Saddle River, New Jersey, 1993.
31. Carr, J. J. Elements of electronic instrumentation and measurements, Third Edition; Prentice-Hall, Upper Saddle River, New Jersey, 1995.
32. Johnson, C. D. Process control instrumentation technology, Fifth Edition; Prentice-Hall, Upper Saddle River, New Jersey, 1997.
33. Edgar, P. Sensors for measurement and control; Longman, London, 1998.
34. Ribbens, W. B. Understanding automotive electronics, Fifth Edition; SAE International, Warrendale, PA, 1998.
35. Webster, J. G. The measurement, instrumentation, and sensors handbook, CRC Press LLC, Boca Raton, Florida, 1999.
36. Pallás-Areny, R.; Webster, J. G. Sensors and signal conditioning, Second Edition; John Wiley & Sons, New York, 2001.
37. Sinclair, I. Sensors and transducers, Third Edition; Newnes, Butterworth-Heinemann, Linacre House, Jordan Hill, Oxford, 2001.
38. Fleming, W. J. Overview of automotive sensors. IEEE Sensors Journal 2001, 1, 296-308.
39. Wilson, J. S. Sensor technology handbook, Newnes, Elsevier Inc., Linacre House, Jordan Hill, Oxford, UK, 2005.
40. Aparicio, F.; Vera, C.; Díaz, V. Teoria de los vehiculos automóviles; Sección de Publicaciones de la ETSII-UPM, Madrid, 1995.
41. Doob, J. L. Stochastic processes; John Wiley & Sons, New York, 1953.
42. Dynkin, E. B. Markov processes; Springer-Verlag, New York, 1965.
43. Papoulis, A.; Pillai, S. U. Probability, random variables, and stochastic processes, Fourth Edition; McGraw-Hill, New York, 2001.
44. Kalman, R. E. A new approach to linear filtering and prediction problems. Trans. ASME, J. Basic Eng. 1960, 82, 35-45.
45. Kalman, R. E.; Bucy, R. S. New results in linear filtering and prediction theory. Trans. ASME, J. Basic Eng. 1961, 83, 95-107.
46. Kalman, R. E. New Results in Wiener Filtering Theory. Proc. Symp. Eng. Appl. Random Functions Theory and Probability; Bobdanoff, J. L., Kozin, F., Ed.; John Wiley & Sons, New York, 1963
47. Sorenson, H. W. Least-squares estimation: from Gauss to Kalman. IEEE Spectrum 1970, 7, 63-68.
48. Kailath, T. A view of three decades of linear filtering theory. IEEE Transactions on Information Theory 1914, 20, 146-181.
49. Anderson, B. D.; Moore, J. B. Optimal Filtering; Dover Publications, New York, 2005.
50. Wiener, N. Extrapolation, Interpolation & Smoothing of Stationary Time Series, The M.I.T. Press, Cambridge, Massachusetts, 1949.
51. Kolmogorov, A. N; Interpolation and Extrapolation, Bull, de l'académie des sciences de U.S.S.R. 1941, Ser. Math. 5, 3-14.
52. Goodwin, G. S.; Sin, K. S. Adaptive Filtering Prediction and Control; Prentice-Hall, Englewood Cliffs, New Jersey, 1984.
53. Widrow, B.; Stearns, S. D. Adaptive Signal Processing, Prentice-Hall, Englewood Cliffs, New Jersey, 1985.
54. Solo, V.; Kong, X. Adaptive Signal Processing Algorithms; Prentice-Hall, Englewood Cliffs, New Jersey, 1995.
55. Haykin, S. Adaptive Filter Theory, Fourth Edition; Prentice-Hall, Upper Saddle River, New Jersey, 2002.
56. Manolakis, D. G.; Ingle, V. K.; Kogon, S. M. Statistical and adaptive signal processing; Artech House, Norwood, MA, 2005.
57. Pan, C. T.; Plemmons, R. J. Least-squares modifications with inverse factorizations: Parallel implications. Journal of Computational an d Applied Mathematics 1989, 27, 109-127.
58. Alexander, S. T. A method for recursive least-squares filtering based upon an inverse QR decomposition. IEEE Transactions on Signal Processing 1993, 41, 20-30.