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Browsing by Subject "Kalman filtering"

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    • Article  

      Bank filters for ML parameter estimation via the Expectation-Maximization algorithm: The continuous-time case 

      Charalambous, Charalambos D.; Logothetis, Andrew; Elliott, Robert J. (1998)
      In this paper we consider continuous-time partially observed systems in which the parameters are unknown. We employ conditional moment generating functions of integrals and stochastic integrals to derive new maximum-likelihood ...

    • Article  

      Certain nonlinear partially observable stochastic optimal control problems with explicit control laws equivalent to LEQG/LQG problems 

      Charalambous, Charalambos D.; Elliott, R. J. (1997)
      This paper is concerned with partially observed stochastic optimal control problems when nonlinearities enter the dynamics of the unobservable state and the observations as gradients of potential functions. Explicit ...

    • Article  

      Exact filters for Newton-Raphson parameter estimation algorithms for continuous-time partially observed stochastic systems 

      Charalambous, Charalambos D.; Hibey, J. L. (2001)
      This paper presents explicit finite-dimensional filters for implementing Newton-Raphson (NR) parameter estimation algorithms. The models which exhibit nonlinear parameter dependence are stochastic, continuous-time and ...

    • Conference Object  

      Feedback control over packet dropping network links 

      Mo, H.; Hadjicostis, Christoforos N. (2007)
      This paper studies stabilization schemes for a discrete-time control systems in which the feedback loop includes a network link that may suffer packet drops. We model the packet dropping network link as an erasure channel ...

    • Article  

      Filtering for linear systems driven by fractional Brownian motion 

      Ahmed, N. U.; Charalambous, Charalambos D. (2003)
      In this paper we study continuous time filtering for linear multidimensional systems driven by fractional Brownian motion processes. We present the derivation of the optimum linear filter equations which involve a pair of ...

    • Conference Object  

      Filtering for linear systems driven by fractional Brownian motion 

      Ahmed, N. U.; Charalambous, Charalambos D. (2000)
      In this paper we study continuous time filtering for linear systems driven by fractional Brownian motion processes. We present the derivation of the optimum linear filter equations which involve a pair of functional-differential ...

    • Article  

      Position and velocity tracking in mobile networks using particle and Kalman filtering with comparison 

      Olama, M. M.; Djouadi, S. M.; Papageorgiou, I. G.; Charalambous, Charalambos D. (2008)
      This paper presents several methods based on signal strength and wave scattering models for tracking a user. The received-signal level method is first used in combination with maximum likelihood (ML) estimation and ...

    • Conference Object  

      State space estimation of a wireless frequency-selective fading channel in Gaussian noise 

      Charalambous, Charalambos D.; Li, X. (2002)
      This paper investigates the possibility of using the state space optimal estimation techniques to estimate the inphase, quadrature and square envelope of a wireless multipath fading channel when the transmitted signal is ...

    • Conference Object  

      Time varying wireless channel modeling, estimation, identification, and power control from measurements 

      Olama, M. M.; Yanyan, L.; Djouadi, S. M.; Charalambous, Charalambos D. (2007)
      This paper is concerned with a time-varying wireless channel modeling, its parameter estimation, system identification, and optimal power control from measurement data. The channel model is represented in state space form, ...

    • Conference Object  

      Wireless fading channels: Modeling, estimation and identification from measurements 

      Charalambous, Charalambos D.; Bultitude, R. J. C.; Zhan, J.; Papageorgiou, I. G. (2005)
      This paper is concerned with modeling and system identification of wireless channels. The models employed are in state space form, while the identification method uses the Expectation-Maximization (EM) algorithm and Kalman ...

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