This paper presents a novel conditionally suboptimal filtering algorithm on estimation problems that arise in discrete nonlinear time-varying stochastic difference systems. The suboptimal state estimate is formed by summing of conditionally nonlinear filtering estimates that their weights depend only on time instants, in contrast to conditionally optimal filtering, the proposed conditionally su...

This paper studies the problem of single-channel noise reduction in the time domain and presents a block-based approach where a vector of the desired speech signal is recovered by filtering a frame of the noisy signal with a rectangular filtering matrix. With this formulation, the noise reduction problem becomes one of estimating an optimal filtering matrix. To achieve such estimation, a method...

A Markovian model for a quantum automata, i.e. an open quantum dynamical system with input and output channels and a feedback is described. A multi-stage version of the theory of quantum measurement and statistical decisions applied to the optimal control problem for quantum dynamical discrete-time objects is developed. Quantum analogies of Stratonovich non-stationary filtering and Bellman quan...

0018-9251/97/$10.00 @ 1997 IEEE Robust estimation, or robust filtering, for uncertain linear systems has been investigated under different conditions in the last two decades (see, for instance, [8, 91 and the references therein). In particular, robust Kalman filtering with respect to uncertain linear systems is still an active research topic that attracts increasing interest, on which several a...

In this paper, we propose a new joint transmit and receive spatial/frequency-domain filtering for single-carrier (SC) multipleinput multiple-output (MIMO) eigenmode transmission using iterative interference cancellation (IC). Iterative IC is introduced to a previously proposed joint transmit and receive spatial/frequency-domain filtering based on minimum mean square error criterion (called join...

Abstract – In this paper we present a unified approach for optimal control and filtering of linear continuous-time singularly perturbed linear systems that facilitates complete and exact decomposition of optimal control and filtering tasks into pure-slow and pure-fast time scales. The presented methodology eliminates numerical ill-conditioning of the original singularly perturbed problems, intr...

This paper presents the optimal joint state filtering and parameter identification problem for linear stochastic time-delay systems with unknown parameters. The original identification problem is reduced to the optimal filtering problem for incompletely measured polynomial (bilinear) timedelay system states over linear observations with an arbitrary, not necessarily invertible, observation matr...

Optimal filtering: The filtering problem involves the estimation of the state vector at time k, given all the measurements up to and including time k, which we denote by z1:k. In a Bayesian setting, this problem can be formalized as the computation of the distribution p(xk|z1:k), which can be done recursively in two steps. In the prediction step, p(xk|z1:k−1) is computed from the filtering dist...