zbMATH — the first resource for mathematics

Geometry Search for the term Geometry in any field. Queries are case-independent.
Funct* Wildcard queries are specified by * (e.g. functions, functorial, etc.). Otherwise the search is exact.
"Topological group" Phrases (multi-words) should be set in "straight quotation marks".
au: Bourbaki & ti: Algebra Search for author and title. The and-operator & is default and can be omitted.
Chebyshev | Tschebyscheff The or-operator | allows to search for Chebyshev or Tschebyscheff.
"Quasi* map*" py: 1989 The resulting documents have publication year 1989.
so: Eur* J* Mat* Soc* cc: 14 Search for publications in a particular source with a Mathematics Subject Classification code (cc) in 14.
"Partial diff* eq*" ! elliptic The not-operator ! eliminates all results containing the word elliptic.
dt: b & au: Hilbert The document type is set to books; alternatively: j for journal articles, a for book articles.
py: 2000-2015 cc: (94A | 11T) Number ranges are accepted. Terms can be grouped within (parentheses).
la: chinese Find documents in a given language. ISO 639-1 language codes can also be used.

a & b logic and
a | b logic or
!ab logic not
abc* right wildcard
"ab c" phrase
(ab c) parentheses
any anywhere an internal document identifier
au author, editor ai internal author identifier
ti title la language
so source ab review, abstract
py publication year rv reviewer
cc MSC code ut uncontrolled term
dt document type (j: journal article; b: book; a: book article)
Recursive estimation of discrete-time signals from nonlinear randomly delayed observations. (English) Zbl 1189.93136
Summary: One-stage prediction, filtering, and fixed-point smoothing problems are addressed for nonlinear discrete-time stochastic systems with randomly delayed measurements perturbed by additive white noise. The observation delay is modelled by a sequence of independent Bernoulli random variables whose values-zero or one-indicate that the real observation arrives on time or it is delayed one sampling time and, hence, the available measurement to estimate the signal is not updated. Assuming that the state-space model generating the signal to be estimated is unknown and only the covariance functions of the processes involved in the observation equation are available, recursive estimation algorithms based on linear approximations of the real observations are proposed.

93E11Filtering in stochastic control
60G35Signal detection and filtering (stochastic processes)
93E10Estimation and detection in stochastic control
94A12Signal theory (characterization, reconstruction, filtering, etc.)
Full Text: DOI
[1] Lu, X.; Zhang, H.; Wang, W.; Teo, K.: Kalman filtering for multiple time-delay systems, Automatica 41, 1455-1461 (2005) · Zbl 1086.93060 · doi:10.1016/j.automatica.2005.03.018
[2] Nilsson, J.; Bernhardsson, B.; Wittenmark, B.: Stochastic analysis and control of real-time systems with random time delays, Automatica 34, 57-64 (1998) · Zbl 0908.93073 · doi:10.1016/S0005-1098(97)00170-2
[3] Kolmanovsky, I. V.; Maizenberg, T. L.: Optimal control of continuous-time linear systems with a time-varying, random delay, Systems control letters 44, 119-126 (2001) · Zbl 0986.93075 · doi:10.1016/S0167-6911(01)00132-3
[4] Yaz, Y. I.; Yaz, E. E.: A new formulation of some discrete-time stochastic-parameter state estimation problems, Applied mathematics letters 10, No. 6, 13-19 (1997) · Zbl 0887.93063 · doi:10.1016/S0893-9659(97)00099-2
[5] Matveev, A. S.; Savkin, A. V.: The problem of state estimation via asynchronous communication channels with irregular transmission times, IEEE transactions on automatic control 48, 670-676 (2003) · Zbl 1025.93019
[6] Sinopoli, B.; Schenato, L.; Franceschetti, M.; Poolla, K.; Jordan, M. I.; Sastry, S. S.: Kalman filtering with intermittent observations, IEEE transactions on automatic control 49, 1453-1464 (2004)
[7] Wang, Z.; Ho, D. W. C.; Liu, X.: Robust filtering under randomly varying sensor delay with variance constraints, IEEE transactions on circuits and systems -- part II 51, No. 6, 320-326 (2004)
[8] Yang, F.; Wang, Z.; Feng, G.; Liu, X.: Robust filtering with randomly varying sensor delay: the finite-horizon case, IEEE transactions on circuits and systems -- part I 56, No. 3, 664-672 (2009)
[9] Nakamori, S.; Caballero-Águila, R.; Hermoso-Carazo, A.; Linares-Pérez, J.: Recursive estimators of signals from measurements with stochastic delays using covariance information, Applied mathematics and computation 162, 65-79 (2005) · Zbl 1062.94520 · doi:10.1016/j.amc.2003.12.066
[10] Nakamori, S.; Hermoso-Carazo, A.; Linares-Pérez, J.: Quadratic estimation of multivariate signals from randomly delayed measurements, Multidimensional systems and signal processing 16, 417-438 (2005) · Zbl 1084.94001 · doi:10.1007/s11045-005-4127-2
[11] Nakamori, S.; Hermoso-Carazo, A.; Linares-Pérez, J.: Least-squares linear smoothers from randomly delayed observations with correlation in the delay, IEICE transactions on fundamentals 89-A, 486-493 (2006)
[12] Chui, C. K.; Chen, G.: Kalman filtering with real-time applications, (1987) · Zbl 0636.93070
[13] Tanizaki, H.: Nonlinear filters. Estimation and applications, (1996) · Zbl 0925.93917
[14] Daum, F.: Nonlinear filters: beyond the Kalman filter, IEEE aerospace and electronic systems magazine 20, 57-69 (2005)
[15] Wang, Z.; Lam, J.; Liu, X.: Filtering for a class of nonlinear discrete-time stochastic systems with state delay, Journal of computational and applied mathematics 201, 153-163 (2007) · Zbl 1152.93053 · doi:10.1016/j.cam.2006.02.009
[16] Calzolari, A.; Florchinger, P.; Nappo, G.: Approximation of nonlinear filters for Markov systems with delayed observations, SIAM journal of control and optimization 45, 599-633 (2006) · Zbl 1109.93043 · doi:10.1137/050623504
[17] Hermoso-Carazo, A.; Linares-Pérez, J.: Extended and unscented filtering algorithms using one-step randomly delayed observations, Applied mathematics and computation 190, 1375-1393 (2007) · Zbl 1117.93070 · doi:10.1016/j.amc.2007.02.016