Journal of Institute of Control, Robotics and Systems (제어로봇시스템학회논문지)

Institute of Control, Robotics and Systems (제어로봇시스템학회)
권호 표지
DOI QR코드

DOI QR Code

Filtering Theory

필터링 이론

  • 송택렬 (한양대학교 전자컴퓨터공학부)
  • Published : 2003.06.01
Download PDF
⟨ Previous Next ⟩

Abstract

The objective of this paper is to survey and put in perspective the existing methods of dynamic filter development. This includes theories and practices for linear and nonlinear filters, multiple model filters, and data association methods for tracking in multitarget environment. The presentation of this paper is motivated by recent surge of interest in the area of designing feedback control systems with reduced number of sensors, detection and identification of abrupt changes, and multitarget tracking in clutter. It is hoped to be useful in view of the need to take a grasp of existing techniques before using them in practice and developing new techniques.

Keywords

References

  1. R. E. Kalman, 'A new approach to linear filtering and prediction problems,' J. Basic Eng. Trans. ASME, vol. 82D, pp. 35-45, 1960
  2. P. S. Maybeck, 'Stochastic Models, Estimation and Control' vol. 2. Academic Press, New York, 1982
  3. A. Papoulis, 'Probability, Random Variables, and Stochastic Process,' New York: McGraw-Hill, 1991
  4. A. H. Jazwinski, 'Stochastic process and filtering theory,' Academic Press, 1970
  5. A. Papoulis, 'Probability and statistics,' Prentice Hall, 1990
  6. G. M. Siouris, 'Optimal control and estimation theory,' John Wiley & Sons. Inc, 1996
  7. S. I. Marcus and A. S. Willsky, 'Algebraic structure and finite dimensional nonlinear estimation,' SIAM J. Math. Analysis, Apr. 1978 https://doi.org/10.1137/0509022
  8. A. Doucet et al., 'Sequential monte carlo methods in practice,' Springer, 2001
  9. M. S, Amlampalam, et al., 'A tutorial on particle filters for online nonlinear/non_Gaussian Bayesian tracking,' IEEE Transactions on Signal Processings, vol. 50, no. 2, Feb. 2002 https://doi.org/10.1109/78.978374
  10. A. Gelb, 'Applied optimal estimation,' MIT Press, 1974
  11. V. J. Aidala, 'Kalman filter behavior in bearings-only-tracking applications,' IEEE Transactions on Aerospace and Electronic Systems, vol. 15, no. 1, pp. 29-39. Jan. 1979 https://doi.org/10.1109/TAES.1979.308793
  12. T. L. Song, J. Y., and C. Park, 'A suboptimal filter design with pseudomeasurements for target tracking,' IEEE Transactions on Aerospace and Electronic Systems, vol. 24, pp. 28-39, Jan. 1988 https://doi.org/10.1109/7.1033
  13. P. L. Bogler, 'Tracking a maneuvering target using input estimation,' IEEE trans. on AES vol. 23, pp. 298-301, May 1987 https://doi.org/10.1109/TAES.1987.310826
  14. T. L. Song, and J. L. Speyer, 'A stochastic analysis of a modified gain extended Kalman filter with applications to estimation with bearings only measurements,' IEEE Transactions on Automatic Control, AC-30, 10, pp. 940-949. Oct. 1985 https://doi.org/10.1109/TAC.1985.1103821
  15. Y. Bar-Shalom and K. Birmiwal, 'Varible dimension filter for maneuvering target tracking,' IEEE Trans. on Aerospace and Electronic Systems, vol. 18, pp. 621-629, Sep. 1982 https://doi.org/10.1109/TAES.1982.309274
  16. A. S. Willsky and H. L. Jones, 'A generalized likelihood ratio approach to the detection and estimation of jumps in linear systems,' IEEE Transaction on Automatic Control, vol. 21, pp. 108-112. 1976 https://doi.org/10.1109/TAC.1976.1101146
  17. D. P. Bertesekas, 'Dynamic programming and stochastic control,' Academic Press. 1976
  18. Y. Bar-Shalom and X. R. Li, 'Estimation with Applications to Tracking and Navigation,' John Wiley & Sons Inc. 2001
  19. D. T. Magill, 'Optimal adaptive estimation of sampled stochastic process,' IEEE Transactions on Automatic Control, vol. 10, pp. 434-439. 1965 https://doi.org/10.1109/TAC.1965.1098191
  20. R. L. Moose, 'An adaptive state estimation solution to the maneuvering target problem,' IEEE Transactions on Automatic Control, vol. 10, pp359-362, 1988 https://doi.org/10.1109/TAC.1975.1100961
  21. P. S. Maybeck and D. P. Halon, 'Performance enhancement of multiple model adaptive estimator,' IEEE Trans. on AES, vol. 31, pp. 1240-1254. Oct. 1995 https://doi.org/10.1109/7.464348
  22. H. A. P. Blom and Y. Bar-Shalom, 'The interacting multiple model algorithm for systems with Markovian switching coefficients,' IEEE Transactions on Automatic Control, 33, pp. 780-783, 1988 https://doi.org/10.1109/9.1299
  23. C. B. Chang and M. Athans, 'State estimation for discrete systems with switching parameters,' IEEE Trans. on AES, vol. 14, no. 5, May 1978 https://doi.org/10.1109/TAES.1978.308603
  24. X. R. Li and Y. Bar-Shalom, 'Design of interacting multiple model for air traffic control tracking,' IEEE Trans. Control System Technology, vol. 1, no. 3. Sep. 1993 https://doi.org/10.1109/87.251886
  25. T. L. Song and D. G. Lee, 'Effective filtering of target glint,' IEEE Trans. on AES, vol. 36, no. 1. Jan. 2000 https://doi.org/10.1109/7.826325
  26. X. R. Li and Y. Bar-Shalom, 'Performance prediction of the interaction multiple model algorithm,' IEEE Trans. on Aerospace and Electronic Systems, vol. 29. pp. 755-771, Jul. 1993 https://doi.org/10.1109/7.220926
  27. Y. Bar-Shalom, K C. Chang., and H. A. P. Blom, 'Tracking a maneuvering target using input estimation versus the interacting multiple model algorithm,' IEEE Trans. on Aerospace and Electronic Systems, vol. 25, pp. 296-300, Mar. 1989 https://doi.org/10.1109/7.18693
  28. X, R. Li and Y. Bar-Shalom, 'MM estimation with variable structure,' IEEE Trans. Automatic Control, vol. 41, pp. 478-493. Apr. 1996 https://doi.org/10.1109/9.489270
  29. X. R. Li, 'Multiple-model estimation with variable structure-Part II : Model-set adaptation,' IEEE Trans. Automat. Control, vol. 45, pp. 2047-2060, Nov. 2002 https://doi.org/10.1109/9.887626
  30. X. R. Li, X. R. Zhi, and Y. M. Zhang, 'Multiple-model estimation with variable structure-Part III : Model-group switching algorithm,' IEEE Transactions on Aerospace and Electronic Systems, vol 35, pp. 225-241, Jan. 1999 https://doi.org/10.1109/7.745694
  31. X. R. Li, X. R. Zhi, and Y. M. Zhang' 'Multiple-model estimation with variable structure-Part IV : Design and evaluation of model-group switching algorithm,' IEEE Transactions on Aerospace and Electronic Systems, vol. 35, pp. 242-254, Jan. 1999 https://doi.org/10.1109/7.745695
  32. X. R. Li, and Y. Bar-Shalom, 'Tracking in clutter with nearest neighbor filters : Analysis and performance,' IEEE Trans. on AES, vol. 32, no. 3, Jul. 1996 https://doi.org/10.1109/7.532259
  33. X. R. Li, 'The pdf of nearest neighbor measurement and a probabilistic nearest neighbor filter for tracking in clutter,' Proceedings of the 32nd CDC, San Antonio, Texas, Dec. 1993 https://doi.org/10.1109/CDC.1993.325011
  34. T. L. Song, and J. H. Ryu, 'A probabilistic nearest neighbor filter for target tracking in a clutter environment,' Proceedings of the AIAA G&C conference. Austin TX, Aug. 2003
  35. Li, N. and Li, X. R., 'Target perceivability and its applications,' IEEE Trans. on Signal Processing, vol. 49, no. 11, Nov. 2001 https://doi.org/10.1109/78.960406
  36. Li, X. R. and N. Li, 'Integrated real-time estimation of clutter density for tracking,' IEEE Trans. on Signal Processing, vol. 48, no. 10, Oct. 2000 https://doi.org/10.1109/78.869029
  37. T. L. Song, and S. J. Shin, 'A probabilistic nearest neighbor filter for m validated measurements,' Proceedings of Fusion 2003, Cairns, Australia, Jul. 2003
  38. X. R. Li, 'Tracking in clutter with strongest neighbor measurements-Part I: Theoretical analysis,' IEEE. Trans. on Automatic Control, vol. 43, no. 11, Nov. 1998 https://doi.org/10.1109/9.728872
  39. X. R. Li and X. R. Zhi, 'PSNF : A refined strongest neighbor filter for tracking in clutter,' Proceedings of the 35th CDC, Kobe Japan, Dec. 1996 https://doi.org/10.1109/CDC.1996.573484
  40. K. J. Rhee and T. L. Song, 'A probabilistic strongest neighbor filter algorithm based on number of validated measurements,' Proceedings of the 16th International Sessions, JSASS, Yokohama, Oct. 2002
  41. Y. Bar-Shalom and T. E. Fortmann, 'Tracking and Data Association,' Academic Press, New York, 1988
  42. T. E. Fortmann, et al., 'Detection thresholds for tracking in clutter - A connection between estimation and signal processing,' IEEE Trans. on Automatic Control, vol. 30, no.3, Mar. 1985 https://doi.org/10.1109/TAC.1985.1103935
  43. S. M Hong and H. S. Shin, 'An analytic approximation of information reduction factor for performance prediction of PDA tracking,' Proceedings of SICE 2002, Aug. 2002. Osaka https://doi.org/10.1109/SICE.2002.1196519
  44. X. R. Li and Y. Bar-Shalom, 'Stability evaluation and track life of the PDAF for tracking in clutter,' IEEE Trans. on Automatic Control, vol. 36, no. 5, May 1991 https://doi.org/10.1109/9.76364
  45. T. Kirubarajan, et al., 'IMMPDAF for radar management and tracking benchmark with ECM,' IEEE Transactions on AES, vol. 34, no. 4, Oct. 1998 https://doi.org/10.1109/7.722696
  46. D. Lerro and Y. Bar-Shalom, 'Interacting multiple model tracking with target amplitude feature,' IEEE Trans. on AES, vol. 29, no. 2, Apr. 1993 https://doi.org/10.1109/7.210086
  47. P. Willett et al., 'Integration of bayes detection with target tracking,' IEEE Trans. on Signal Processing, vol. 49, no. 1, Jan. 2001 https://doi.org/10.1109/78.890334
  48. Y. Bar-Shalom and X. R. Li, 'Multitarget - multisensor tracking' Storrs, CT : YBS Publishing, 1995
  49. R. J. Fitzgerald, 'Development of practical PDA logic for multi target tracking by microprocessor,' Multitarget-Multisensor tracking, Artech House, 1990
  50. R. Helmick, 'IMM Estimation with nearest neighbor joint probabilistic data association,' Multi targer-Multisensor Tracking vol. 3, Artech House, 2000
  51. S. S. Blackman, 'Multiple-target tracking with radar applications,' Artech House, 1986
  52. S. Deb, et al., 'A generalized S-D assignment algorithm for muitisensor-muititarget state estimation,' IEEE Transactions on Aerospace and Electronic Systems, vol. 33, no. 2, Apr. 1997 https://doi.org/10.1109/7.575891

Cited by

  1. Track-to-Track Information Fusion using 2D and 3D Radars vol.18, pp.9, 2012, https://doi.org/10.5302/J.ICROS.2012.18.9.863
  2. Multi-target Tracking Filters and Data Association: A Survey vol.20, pp.3, 2014, https://doi.org/10.5302/J.ICROS.2014.14.9018
  3. A Study on the GPS/INS Integration and GPS Compensation Algorithm Based on the Particle Filter vol.50, pp.6, 2013, https://doi.org/10.5573/ieek.2013.50.6.267