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This paper contains a description of methods and algorithms for solving the generalization problem in intelligent decision support systems. For this purpose the argumentation approach for inductive concept formation is used. The methods for finding the conflicts and the generalization algorithm based on the rough set theory are proposed. It is suggested to use the argumentation, based on defeasible...
The paper deals with state estimation of nonlinear stochastic dynamic systems. In particular, the stress is laid on development of second-order discrete-time filters for nonlinear continuous-discrete models. Two different filters are derived based on different order of nonlinear function approximation and discretization of the continuous dynamics equation. For the approximation the Taylor expansion...
Since the groundbreaking work of the Kalman filter in the 1960s, considerable effort has been devoted to discrete time filters for dynamic state estimation, especially including a variety of suboptimal implementations of the Bayesian filter. The essence of the Bayesian filter is to make the (sub)optimum fusion of the observation information in time sequence based on the hidden Markov model of the...
A fusion methodology for tracks represented by Gaussian mixtures is proposed for distributed maneuvering target tracking with unknown correlation information between the local agents. For this purpose, Chernoff fusion is applied to the Gaussian mixtures provided by the local interacting multiple-model (IMM) filters. Chernoff fusion of Gaussian mixtures is achieved using a recently proposed method...
We propose a novel measurement update procedure for orientation estimation algorithms that are based on directional statistics. This involves consideration of two scenarios, orientation estimation in the 2D plane and orientation estimation in three-dimensional space. We make use of the von Mises distribution and the Bingham distribution in these scenarios. In the derivation, we discuss directional...
Recent research has provided several new methods for avoiding degeneracy in particle filters. These methods implement Bayes' rule using a continuous transition between prior and posterior. The feedback particle filter (FPF) is one of them. The FPF uses feedback gains to adjust each particle according to the measurement, which is in contrast to conventional particle filters based on importance sampling...
The Ensemble Kalman Filter (EnKF) is a Kalman based particle filter which was introduced to solve large scale data assimilation problems where the state space is of very large dimensionality. It also achieves good results when applied to a target tracking problem, however, due to its Gaussian assumption for the prior density, the performance can be improved by introducing Gaussian mixtures. In this...
A graph is used to represent data in which the relationships between the objects in the data are at least as important as the objects themselves. Large graph datasets are becoming more common as networks such as the Internet grow, and our ability to measure these graphs improves. This necessitates methods to compress these datasets. In this paper we present a method aimed at lossy compression of large,...
Baseline in signals is a relatively complicated problem in analysis of signals obtained in various analytical techniques such as chromatography and spectroscopy. In this article there are presented results of tests on four algorithms for a baseline estimation in chromatographic signals. Two of them are based on a polynomial fitting in a region of detected peaks. Another two algorithms, i.e. assymetric...
Compressed sensing (CS) or compressive sampling deals with reconstruction of signals from limited observations/ measurements far below the Nyquist rate requirement. This is essential in many practical imaging system as sampling at Nyquist rate may not always be possible due to limited storage facility, slow sampling rate or the measurements are extremely expensive e.g. magnetic resonance imaging (MRI)...
A spectral shaping technique is adopted to expand the operating frequency range for DWT-based blind audio watermarking. The process is performed framewisely over the 3rd level approximation subband, of which the spectrum spans from 0 to 2756 Hz. The effectiveness of the proposed scheme is demonstrated using the perceptual evaluation of audio quality and bit error rates of recovered watermarks under...
Parallelizability of an algorithm is nowadays a highly desirable property as computer hardware is becoming increasingly parallel. In this paper, a formulation of the particle filtering algorithm, suitable for parallel or distributed computing, is proposed. From the particle set, a series expansion is fitted to the posterior probability density function. The global information provided by the particles...
Discrete-time estimation of rigid body attitude and angular velocity without any knowledge of the attitude dynamics model, is treated using the discrete Lagrange-d'Alembert principle. Using body-fixed sensor measurements of direction vectors and angular velocity, a Lagrangian is obtained as the difference between a kinetic energy-like term that is quadratic in the angular velocity estimation error...
A measurement-based statistical verification approach is developed for systems with partly unknown dynamics. These grey-box systems are subject to identification experiments which, new in this contribution, enable accepting or rejecting system properties expressed in a linear-time logic. We employ a Bayesian framework for the computation of a confidence level on the properties and for the design of...
This paper presents a data-driven control scheme to iteratively achieve the desired objective criterion with significant improvement of the convergence performance for linear-time-invariant (LTI) single-input-single-output (SISO) systems. The internal iterative behavior between the current parameter and the optimal parameter is firstly analyzed with mathematic expression. And a novel iterative law...
In this paper, we present an open-loop Stochastic Model Predictive Control (SMPC) method for discrete-time nonlinear systems whose state is defined on the unit circle. This modeling approach allows considering systems that include periodicity in a more natural way than standard approaches based on linear spaces. The main idea of this work is twofold: (i) we model the quantities of the system, i.e...
The problem of finding the optimal sensor location is considered for a Kalman filter used to estimate the state of an one-dimensional linear dispersive wave equation. Various bases for a Galerkin approximation were studied: sine functions, linear finite elements and a sixth order polynomial finite element basis. The calculated estimator and the optimal sensor location converge for all the bases. For...
In this paper, we propose a new method of secrecy signal and artificial noise (AN) transmission for a MISO wiretap channel in wireless cellular networks. We first design the secrecy signal beamformer such that its power leakage to the unintended directions is minimized, then we concentrate the AN on the directions with higher risk of information leakage, rather than broadcast it isotropically, such...
Most of the existing sparse recovery methods are based on the squared error criterion, i.e., ℓ2-norm metric, by appropriately adding to a sparsity-promoting regularizer. This criterion is, however, statistically optimal only when the noise are Gaussian distributed. In fact, non-Gaussian impulsive noise with heavy tailed distribution has been reported in a variety of practical applications. To guarantee...
In this paper, we present an optimal filter for linear time-invariant continuous-time stochastic systems that simultaneously estimates the states and unknown inputs in an unbiased minimum-variance sense. The optimality of the proposed filter is proven by reduction to an equivalent system without unknown inputs. Then, a second proof is given for a special case by limiting case approximations of the...
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