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An application of the expectation-maximization algorithm to interference rejection for direct-sequence spread-spectrum signals

Quan G. Zhang, Costas N. Georghiades (1999)

Kybernetika

For a direct-sequence spread-spectrum (DS-SS) system we pose and solve the problem of maximum-likelihood (ML) sequence estimation in the presence of narrowband interference, using the expectation-maximization (EM) algorithm. It is seen that the iterative EM algorithm obtains at each iteration an estimate of the interference which is then subtracted from the data before a new sequence estimate is produced. Both uncoded and trellis coded systems are studied, and the EM-based algorithm is seen to perform...

An efficient algorithm for estimating the parameters of superimposed exponential signals in multiplicative and additive noise

Jiawen Bian, Huiming Peng, Jing Xing, Zhihui Liu, Hongwei Li (2013)

International Journal of Applied Mathematics and Computer Science

This paper considers parameter estimation of superimposed exponential signals in multiplicative and additive noise which are all independent and identically distributed. A modified Newton-Raphson algorithm is used to estimate the frequencies of the considered model, which is further used to estimate other linear parameters. It is proved that the modified Newton-Raphson algorithm is robust and the corresponding estimators of frequencies attain the same convergence rate with Least Squares Estimators...

An SFDI observer-based scheme for a general aviation aircraft

Marco Ariola, Massimiliano Mattei, Immacolata Notaro, Federico Corraro, Adolfo Sollazzo (2015)

International Journal of Applied Mathematics and Computer Science

The problem of detecting and isolating sensor faults (sensor fault detection and isolation-SFDI) on a general aviation aircraft, in the presence of external disturbances, is considered. The proposed approach consists of an extended Kalman observer applied to an augmented aircraft plant, where some integrators are added to the output variables subject to faults. The output of the integrators should be ideally zero in the absence of model uncertainties, external disturbances and sensor faults. A threshold-based...

Approximation and estimation in Markov control processes under a discounted criterion

J. Adolfo Minjárez-Sosa (2004)

Kybernetika

We consider a class of discrete-time Markov control processes with Borel state and action spaces, and ℜ k -valued i.i.d. disturbances with unknown density ρ . Supposing possibly unbounded costs, we combine suitable density estimation methods of ρ with approximation procedures of the optimal cost function, to show the existence of a sequence { f ^ t } of minimizers converging to an optimal stationary policy f ∞ .

Approximation, estimation and control of stochastic systems under a randomized discounted cost criterion

Juan González-Hernández, Raquiel R. López-Martínez, J. Adolfo Minjárez-Sosa (2009)

Kybernetika

The paper deals with a class of discrete-time stochastic control processes under a discounted optimality criterion with random discount rate, and possibly unbounded costs. The state process x t and the discount process α t evolve according to the coupled difference equations x t + 1 = F ( x t , α t , a t , ξ t ) , α ...

Asynchronous distributed state estimation for continuous-time stochastic processes

Zdzisław Kowalczuk, Mariusz Domżalski (2013)

International Journal of Applied Mathematics and Computer Science

The problem of state estimation of a continuous-time stochastic process using an Asynchronous Distributed multi-sensor Estimation (ADE) system is considered. The state of a process of interest is estimated by a group of local estimators constituting the proposed ADE system. Each estimator is based, e.g., on a Kalman filter and performs single sensor filtration and fusion of its local results with the results from other/remote processors to compute possibly the best state estimates. In performing...

Bayesian estimation of mixtures with dynamic transitions and known component parameters

Ivan Nagy, Evgenia Suzdaleva, Miroslav Kárný (2011)

Kybernetika

Probabilistic mixtures provide flexible “universal” approximation of probability density functions. Their wide use is enabled by the availability of a range of efficient estimation algorithms. Among them, quasi-Bayesian estimation plays a prominent role as it runs “naturally” in one-pass mode. This is important in on-line applications and/or extensive databases. It even copes with dynamic nature of components forming the mixture. However, the quasi-Bayesian estimation relies on mixing via constant...

Bayesian estimation of the mean holding time in average semi-Markov control processes

J. Adolfo Minjárez-Sosa, José A. Montoya (2015)

Applicationes Mathematicae

We consider semi-Markov control models with Borel state and action spaces, possibly unbounded costs, and holding times with a generalized exponential distribution with unknown mean θ. Assuming that such a distribution does not depend on the state-action pairs, we introduce a Bayesian estimation procedure for θ, which combined with a variant of the vanishing discount factor approach yields average cost optimal policies.

Bayesian parameter estimation and adaptive control of Markov processes with time-averaged cost

V. Borkar, S. Associate (1998)

Applicationes Mathematicae

This paper considers Bayesian parameter estimation and an associated adaptive control scheme for controlled Markov chains and diffusions with time-averaged cost. Asymptotic behaviour of the posterior law of the parameter given the observed trajectory is analyzed. This analysis suggests a "cost-biased" estimation scheme and associated self-tuning adaptive control. This is shown to be asymptotically optimal in the almost sure sense.

Canonical input-output representation of linear multivariable stochastic systems and joint optimal parameter and state estimation.

G. Salut, J. Aguilar-Martín, S. Lefevre (1979)

Stochastica

In this paper a complete presentation is given of a new canonical representation of multi-input, multi-output linear stochastic systems. Its equivalence with operator form directly linked with ARMA processes as well as with classical state space representation is given, and a transfer matrix interpretation is developed in an example. The importance of the new representation is mainly in the fact that in the joint state and parameters estimation problem, all unknown parameters appear linearly when...

Completitud esencial de la clase de controles basados en un proceso suficiente.

Pilar Ibarrola Muñoz, Javier Yáñez Gestoso (1985)

Trabajos de Estadística e Investigación Operativa

Se define en este artículo el concepto de proceso suficiente para un proceso de control, así como el de control basado en un proceso suficiente. Se demuestra a continuación que el conjunto de controles basados en un proceso suficiente forma una clase esencialmente completa; por consiguiente, dado un control, existe un control basado en el proceso suficiente que tiene el mismo coste esperado que el anterior.

Configuring a sensor network for fault detection in distributed parameter systems

Maciej Patan, Dariusz Uciński (2008)

International Journal of Applied Mathematics and Computer Science

The problem of fault detection in distributed parameter systems (DPSs) is formulated as that of maximizing the power of a parametric hypothesis test which checks whether or not system parameters have nominal values. A computational scheme is provided for the design of a network of observation locations in a spatial domain that are supposed to be used while detecting changes in the underlying parameters of a distributed parameter system. The setting considered relates to a situation where from among...

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