this paper studies the use of unscented kalman filters (ukf) to estimate nonlinear dynamics and, specifically, adaptive determination of scaling parameters in these filters. due to lack of analytic solution and use of numerical methods instead, the computational load of these filters increases drastically. in this paper, a new method is proposed based on interactive multiple models (imm) which ...

Nonlinear loads generate harmonic currents and voltages in power systems. This paper investigates the effect of inverter output multiple-filter with nonlinear loads on the inverter input current and load voltage. To verify the proposed filter, various simulation results using Matlab/Simulink are presented under nonlinear loads.

This paper presents the central finite-dimensional H∞ filter for nonlinear polynomial systems, that is suboptimal for a given threshold γ with respect to a modified BolzaMeyer quadratic criterion including the attenuation control term with the opposite sign. In contrast to the previously obtained results, the paper reduces the original H∞ filtering problem to the corresponding optimal H2 filter...

We present recent advances in the development of nonlinear extensions to the minimum average correlation energy (MACE) filter. The MACE filter and its variations have been applied to the area of automatic target detection and recognition (ATD/R). Nonlinear extensions (Fisher and Principe, 1994) have been presented based on a statistical formulation of the optimization criterion, of which the li...

This paper investigates the nonlinear effects of the LeastMean Square (LMS) adaptive predictor. Traditional analysis of the adaptive filter ignores the statistical dependence among successive tap-input vectors and bounds the performance of the adaptive filter by that of the finite-length Wiener filter. It is shown that the nonlinear effects make it possible for an adaptive transversal predictio...

Nonlinear filtering is certainly very important in estimation since most real-world problems are nonlinear. Recently a considerable progress in the nonlinear filtering theory has been made in the area of the sampling-based methods, including both random (Monte Carlo) and deterministic (quasi-Monte Carlo) sampling, and their combination. This work considers the problem of tracking a maneuvering ...

Stabilization using filters is intended to model and extract the energy lost to resolved scales due to nonlinearity breaking down resolved scales to unresolved scales. This process is highly nonlinear and yet current models for it use linear filters to select the eddies that will be damped. In this report we consider for the first time nonlinear filters which select eddies for damping (simulati...

This paper presents the theoretical development of a nonlinear adaptive filter based on a concept of filtering by approximated densities (FAD). Themost common procedures for nonlinear estimation apply the extended Kalman filter. As opposed to conventional techniques, the proposed recursive algorithm does not require any linearisation. The prediction uses a maximum entropy principle subject to c...

Nonlinear state estimation and fusion tracking are always hot research topics for information processing. Compared to linear fusion tracking, nonlinear fusion tracking takes many new problems and challenges. Especially, the performances of fusion tracking, based on different nonlinear filters, are obviously different. The conventional nonlinear filters include extended Kalman filter (EKF), unsc...