Skip to main content

On Detection-Estimation Schemes for Uncertain Systems

  • Chapter
Communications and Networks
  • 159 Accesses

Abstract

Combined detection-estimation schemes for stochastic signals have been considered as early as the mid-1960s (see, e. g., [1–5]). In general three basic problems may be classified as joint detection-estimation problems: (1) It is desired to detect a signal when some or all of its parameters or model are unknown. The result is a decision-directed estimation where the ultimate interest is in the detection outcome, while the estimate of the parameters or states may only be a by-product, which need not be obtained. In many robust detection problems, these variables are not estimated explicitly, and the concern is in making the detector performance insensitive to the unknown variables. (2) It is desired to estimate a signal in the presence of several uncertainties which take on discrete (finitely many) values. In this case, the resulting estimator is concerned primarily with the estimate of the signal attributes, and the detection of the precise mode of uncertainty governing the signal model is a by-product of the estimation process. In most cases, one is satisfied with the estimation outcome without explicitly identifying or detecting the modes involved. (3) A truly joint estimation-detection scheme is concerned with detecting the presence of a signal and estimating its parameters at the same time, and the performance criterion used in such a case is a coupled one yielding a detector and estimator which depend on each other. The first problem is not discussed here, and only a brief exposition of the third problem will be given. The main part of this chapter is concerned with the joint detection- estimation schemes for estimation purposes. The discussion of the third problem will be limited to its relationship to the estimation problem.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

eBook
USD 16.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. D. Middleton R. Esposito Simultaneous optimum detection and estimation of signals in noise,IEEE Trans. Inform. Theory, Vol. IT–14, pp. 434–444, 1968.

    Article  MATH  Google Scholar 

  2. D.T. Magill Optimal adaptive estimation of sampled stochastic processes, IEEE Trans. Auto. Contr., Vol. AC-10, pp. 434–4391965

    Google Scholar 

  3. D.G. Lainiotis, Joint detection, estimation and system identification, Inform. Contr., Vol. 19, pp. 75–92, 1971.

    Article  MathSciNet  MATH  Google Scholar 

  4. A. Fredricksen D. Middleton VD VandeLinde Simultaneous signal detection and estimation under multiple hypotheses, IEEE Trans. Inform. Theory, Vol. IT–18, pp. 607–614, 1972.

    Google Scholar 

  5. D.W. Kelsey and A.H. Haddad, Detection and prediction of a stochastic process having multiple hypotheses, Inform. Sci., Vol. 6, pp. 301–311, 1973.

    Article  MathSciNet  Google Scholar 

  6. J.O. Pearson, Estimation of uncertain systems, in Control and Dynamic Systems, Vol. 10, C.T. Leondes, ed., New York, NY: Academic Press, 1973.

    Google Scholar 

  7. J.A. D’Appolito and C.E. Hutchinson, A minimax approach to the design of low sensitivity state estimators, Automatica, Vol. 8, pp. 599–608, 1972.

    Article  MathSciNet  MATH  Google Scholar 

  8. R.K. Mehra, Approaches to adaptive filtering, IEEE Trans. Auto. Contr., Vol. AC–17, pp. 693–698, 1972.

    Google Scholar 

  9. D.G. Lainiotis, Optimal adaptive estimation: Structure and parameter adaptation, IEEE Trans. Auto. Contr., Vol. AC–16, pp. 160–170, 1971.

    Google Scholar 

  10. Y. Bar-Shalom, Optimal simultaneous state estimation and parameter identification in linear discrete-time systems, IEEE Trans. Auto. Contr., Vol. AC–17, pp. 308–319, 1972.

    Google Scholar 

  11. D.G. Lainiotis, Partitioning: A unifying framework for adaptive systems, I: Estimation, Proc. IEEE, Vol. 64, pp. 1126–1142, 1976.

    Article  MathSciNet  Google Scholar 

  12. D.G. Lainiotis, Partitioning: A unifying framework for adaptive systems, II: Control, Proc. IEEE, Vol. 64, pp. 1182–1198, 1976.

    Article  MathSciNet  Google Scholar 

  13. D.W. Kelsey and A.H. Haddad, A note on detectors for joint minimax detection-estimation schemes, IEEE Trans. Auto. Contr., Vol. AC–18, pp. 558–559, 1973.

    Google Scholar 

  14. N.E. Nahi, Optimal recursive estimation with uncertain observations, IEEE Trans. Inform. Theory, Vol. IT–15, pp. 457–462, 1969.

    Google Scholar 

  15. A.G. Jaffer and S.C. Gupta, Optimal sequential estimation of discrete processes with Markov interrupted observations, IEEE Trans. Auto. Contr., Vol. AC–16, pp. 471–475, 1971.

    Google Scholar 

  16. Y. Sawaragi T. Katayama S. Fujishige Adaptive estimation for a linear system with interrupted observation IEEE Trans. Auto. Contr., Vol. AC–18, pp. 152–154, 1973.

    Article  Google Scholar 

  17. G.A. Ackerson K.S. Fu On state estimation in switching environments,IEEE Trans. Auto. Contr., Vol. AC-15, pp. 10–17, 1970.

    Article  Google Scholar 

  18. M.T. Hadidi S.C. Schwartz Linear recursive state estimators under uncertain observations IEEE Trans. Auto. Contr., Vol. AC–24, pp. 944–948, 1979.

    Article  MathSciNet  MATH  Google Scholar 

  19. D.G. Lainiotis, Partitioned estimation algorithms, I: Nonlinear estimation, Inform. Set, Vol. 7, pp. 203–255, 1974.

    Google Scholar 

  20. D.G. Lainiotis and S.K. Park, On joint detection, estimation and system identification: Discrete data case, Int. J. Control, Vol. 17, pp. 609–633, 1973.

    Article  MathSciNet  MATH  Google Scholar 

  21. A.H. Haddad and J.B. Cruz, Jr., Nonlinear filtering for systems with random models, Proc. 2nd Symposium on Nonlinear Estimation Theory, pp. 147–150, 1971.

    Google Scholar 

  22. D.G. Lainiotis, Partitioned linear estimation algorithms: Discrete case, IEEE Trans. Auto. Contr., Vol. AC–20, pp. 255–257, 1975.

    Google Scholar 

  23. L.A. Liporace, Variance of Bayes estimates, IEEE Trans. Inform. Theory, Vol. IT–7, pp. 665–669, 1971.

    Google Scholar 

  24. R.M. Hawkes and J.R. Moore, Performance of Bayesian parameter estimators for linear signal models, IEEE Trans. Auto. Contr., Vol. AC–21, pp. 523–527, 1976.

    Google Scholar 

  25. R.M. Hawkes and J.R. Moore, Performance bounds for adaptive estimation, Proc. IEEE, Vol. 64, pp. 1143–1150, 1976.

    Article  MathSciNet  Google Scholar 

  26. A.V. Sebald and A.H. Haddad, Robust state estimation in uncertain systems: Combined detection estimation with incremental MSE criterion, IEEE Trans. Auto. Contr., Vol. AC-22, pp. 821–825, 1977.

    Google Scholar 

  27. R.A. Padilla and A.H. Haddad, On the estimation of uncertain signals using an estimation-detection scheme, IEEE Trans. Auto. Contr., Vol. AC–21, pp. 509–512, 1976.

    Google Scholar 

  28. J.K. Tugnait and A.H. Haddad, On state estimation for linear discrete- time systems with unknown noise covariances, IEEE Trans. Auto. Contr., Vol. AC–24, pp. 337–340, 1979.

    Google Scholar 

  29. A.V. Sebald and A.H. Haddad, State estimation for singularly perturbed systems with uncertain perturbation parameter, IEEE Trans. Auto. Contr., Vol. AC–23, pp. 464–469, 1978.

    Google Scholar 

  30. A.V. Sebald and T. Takenawa, Optimal state estimation in the presence of deterministic perturbations of uncertain structure occurring at unknown times, Proc. 18th IEEE Conf. Dec. Contr., pp. 488–493, 1979.

    Google Scholar 

  31. A.G. Jaffer and S.C. Gupta, On estimation of discrete processes under multiplicative and additive noise conditions, Inform. Sci., Vol. 3, pp. 267–276, 1971.

    Article  MathSciNet  MATH  Google Scholar 

  32. M. Askar, H. Derin and H.O. Yurtseven, Recursive estimation of Gauss-Markov process using uncertain observations, Proc. Twelfth Annual Asilomar Conf. on Circ. Syst. and Comp., California, pp. 731–735, 1978.

    Google Scholar 

  33. M. Askar and H. Derin, A recursive algorithm for the Bayes solution of the smoothing problem, IEEE Trans. Auto. Contr., Vol. AC–26, pp. 558–561, 1981.

    Google Scholar 

  34. M. Askar and H. Derin, Recurvise algorithm for the Bayes solution of the smoothing with uncertain observations, Proc. 1983 ACC, San Francisco, CA, pp. 108-110, 1983.

    Google Scholar 

  35. R.A. Monzingo, Discrete linear recursive smoothing for systems with uncertain obsesrvations, IEEE Trans. Auto. Contr., Vol. AC–26, pp. 754–757, 1981.

    Google Scholar 

  36. A.G. Jaffer and S.C. Gupta, Recursive Bayesian estimation with uncertain observation, IEEE Trans. Inform. Theory, Vol. IT–17, pp. 614–616, 1971.

    Google Scholar 

  37. R.A. Monzingo, Discrete optimal linear smoothing for systems with uncertain observations, IEEE Trans. Inform. Theory, Vol. IT–21, pp. 271–275, 1975.

    Google Scholar 

  38. J.K. Tugnait, On identification and adaptive estimation for systems with interrupted observations, Automatica, Vol. 19, pp. 61–73, 1983.

    Article  MathSciNet  MATH  Google Scholar 

  39. C.B. Chang and M. Athans, State estimation for discrete systems with switching parameters, IEEE Trans. Aero. Elec. Sys., Vol. AES–14, pp. 418–425, 1978.

    Google Scholar 

  40. H. Akashi and H. Kumamoto, Random sampling approach to state estimation in switching environments, Automatica, Vol. 13, pp. 429–434, 1977.

    Article  MATH  Google Scholar 

  41. A. Wernerson, On Bayesian estimators for discrete-time linear systems with Markovian parameters, Proc. 6th Symposium on Nonlinear Estimation Theory and Its Applications, San Diego, CA, pp. 253–263, 1975.

    Google Scholar 

  42. H.F. Van Landingham, R.L. Moose and W.H. Lucas, Modelling and control of nonlinear plants, Proc. 17th IEEE Conf. Dec. Contr., pp. 337–341, 1979.

    Google Scholar 

  43. Y. Bar-Shalom, Tracking methods in a multi-target environment, IEEE Trans. Auto. Contr., Vol. AC–23, pp. 618–626, 1978.

    Google Scholar 

  44. J.K. Tugnait and A.H. Haddad, A detection-estimation scheme for state estimation in switching environments, Automatica, Vol. 15, pp. 477–481, 1979.

    Article  MathSciNet  MATH  Google Scholar 

  45. T. Kailath, The divergence and Bhattacharyya distance measures in signal selection, IEEE Trans. Comm. Tech., Vol. COM–15, pp. 52–60, 1967.

    Google Scholar 

  46. J.K. Tugnait and A.H. Haddad, State estimation under uncertain observations with unknown statistics, IEEE Trans. Auto. Contr., Vol. AC–24, pp. 201–210, 1979.

    Google Scholar 

  47. J.K. Tugnait and A.H. Haddad, Adaptive estimation in linear systems with unknown Markovian noise statistics, IEEE Trans. Inform. Theory, Vol. IT–26, pp. 66–78, 1980.

    Google Scholar 

  48. J.K. Tugnait, Adaptive estimation and identification for discrete systems with Markov jump parameters, IEEE Trans. Auto. Contr., Vol. AC–27, pp. 1054–1065, 1982.

    Google Scholar 

  49. R.L. Moose, H.F. Van Landingham and D.H. McCabe, Applications of adaptive state estimation theory, Proc. 19th IEEE Conf. Dec. Contr., pp. 568–575, 1980.

    Google Scholar 

  50. M. Athans et al., The stochastic control of F-8C aircraft using a multiple model adaptive control (MMAC) method: Part I; Equilibrium flight,IEEE Trans. Auto. Contr., Vol. AC–22, pp. 768–780, 1977.

    Google Scholar 

  51. C.M. Brown, Jr. and C.F. Price, A comparison of adaptive tracking filters for targets of variable maneuverability, Proc. IEEE Conf. Dec. and Contr., pp. 554–563, 1976.

    Google Scholar 

  52. R.L. Moose, An adaptive state estimator solution to the maneuvering target problems, IEEE Trans. Auto. Contr., Vol. AC–20, pp. 359–362, 1975.

    Google Scholar 

  53. J.S. Thorp, Optimal tracking of maneuvering targets, IEEE Trans. Aero. Elec. Sys., Vol. AES–9, pp. 512–519, 1973.

    Google Scholar 

  54. J.D. Birdwell, D.A. Castanon and M. Athans, On reliable control system designs with and without feedback reconfigurations, Proc. 17th IEEE Conf. Dec. Contr., pp. 419–426, 1979.

    Google Scholar 

  55. A.S. Willsky, A survey of design methods for failure detection in dynamic systems, Automatica, Vol. 12, pp. 601–611, 1976.

    Article  MathSciNet  MATH  Google Scholar 

  56. A.K. Caglayan, Simultaneous failure detection and estimation in linear systems, Proc. 19th IEEE Conf. Dec. and Contr., pp. 1038–1041, 1980.

    Google Scholar 

  57. S.P. Au and A.H. Haddad, Suboptimal sequential estimation-detection scheme for Poisson driven linear systems, Inform. Sci., Vol. 16, pp. 95–113, 1978

    Article  MathSciNet  MATH  Google Scholar 

  58. H. Kwakernaak, Filters for systems excited by Poisson white noise, Control Theory, Numerical Methods, and Computer Systems Modelling (Lecture Notes in Economics and Math. Systems, Vol. 107), A. Bensoussan and J.L. Lions, Eds., Berlin, Germany: Springer-Verlag, 1975.

    Google Scholar 

  59. H. Kwakernaak, An estimate of the variance reduction in filtering for linear systems excited by Poisson white noise, Dep. Appl. Math., Twente Univ. Technol., Erischede, The Netherlands, Memo. 56, October, 1974.

    Google Scholar 

  60. S.P. Au, A.H. Haddad and H.V. Poor, A state estimation algorithm for linear systems driven simultaneously by Wiener and Poisson processes, IEEE Trans. Auto. Contr., Vol. AC-27, pp. 617–626, 1982.

    Google Scholar 

  61. A.H. Haddad J.K. Tugnait On state estimation using detection- estimation schemes for uncertain systems Proc. JACC, pp. 514–519, Denver, CO, 1979.

    Google Scholar 

  62. J.K. Tugnait, Detection and estimation for abruptly changing systems, Automatica, Vol. 18, pp. 607–615, 1982.

    Article  MATH  Google Scholar 

  63. D.G. Lainiotis, Estimation: A brief survey, Inform. Sci., Vol. 7, pp. 197–202, 1974.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 1986 Springer-Verlag New York Inc.

About this chapter

Cite this chapter

Haddad, A.H. (1986). On Detection-Estimation Schemes for Uncertain Systems. In: Blake, I.F., Poor, H.V. (eds) Communications and Networks. Springer, New York, NY. https://doi.org/10.1007/978-1-4612-4904-7_3

Download citation

  • DOI: https://doi.org/10.1007/978-1-4612-4904-7_3

  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-1-4612-9354-5

  • Online ISBN: 978-1-4612-4904-7

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics