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In this paper, in order to estimate the operational parameters of synchronous generator model suitable for unbalanced conditions, an approach which uses Trajectory Sensitivity Functions (TSF) and is based on Unscented Kalman Filter (UKF) is developed. In this framework, iniatilly, TSF are used to identify the parameters which most affect the behavior of the system. After the ordination of the parameters...
For SAR-GMTI system, efficient motion parameters estimation is a key challenge for moving targets imaging and localization, especially in low SNR scenarios. By four dimension (4-D) parameters searching, the Radon FRFT can achieve motion parameters estimation even when the SNR is low. But the 4-D searching operation brings heavy computation load. Focusing on these, we propose an efficient Radon FRFT...
Aim of this study is to explore the applicability of patient reported outcome (PRO) scale to the efficacy evaluation of acupuncture for cervical spondylosis. Multidimensional three-parameter logistic model and multidimensional graded response model were established by programming autonomously with SF-36 scale. Markov chain Monte Carlo algorithm and expectation maximization algorithm were adopted to...
Since most distributed estimation algorithms only try to achieve high estimation precision while ignoring the positive-negative problem of components in the true parameter, estimation using these methods may be physically absurd and uninterpretable. In order to avoid erroneous results, we need to add a nonnegative constraint on the parameter to be estimated. In this paper, we propose a novel distributed...
This paper aims at developing a management system for a lithium-ion battery in order to monitor its state-of-health. Based on the electrochemical processes within the battery, state-of-health indicators are deduced from the solid phase diffusion coefficients that describe the propagation of lithium in each electrode. Hence the estimation of these diffusion coefficients is the focus of the paper. The...
This paper presents, for the first time in literature, an approach and preliminary results for the design of dynamic experiments in the framework of bounded-error (guaranteed) parameter estimation that determines confidence limits on the identified parameters similarly to a posteriori analysis in standard maximum-likelihood (e.g. least-squares) estimation. As in the classical approaches to the design...
Retrospective estimation of the process noise covariance is performed by minimizing the cumulative state-estimation error based on the innovations. This technique is applied to parameter estimation problems, where the parameters to be estimated are time-varying and thus do not fit in the classical Kalman filter noise structure. This technique is compared to the standard Kalman filter with a fixed...
This paper considers the problem of estimating the parameters of a signal using time-varying thresholded noisy one-bit measurements. The problem is shown to be deterministically identifiable under reasonable conditions on the signal and thresholds. A spectral sensing application is considered, and two sparse methods are presented. In addition to the standard ℓ1 norm based approach, a “zero-norm” approximation...
This paper proposes a parameter estimator for four-wheel-independently driven electric (4 WID) vehicle with in-wheel motors. The mass and location of payload have great impacts on the stability and maneuverability of lightweight vehicles (LWVs). Fast, effective and real-time parameter estimator can make the existing controller adjust the changed parameter caused by additional payload. The proposed...
This paper addresses the problem of estimating states and parameters of chaotic memristive systems with single measurable variable. Based on the conventional extended Kalman filter, we present two new strategies based on the joint extended Kalman filter and dual extended Kalman filter. Then, the two kinds of filters are employed to estimate the states and parameters of chaotic memristive systems....
It is attractive for wideband Radar system that sampling rate can be reduced by compressed sensing method. The GTD model can simulate wideband scattering with high accuracy. A compressed sensing method for parameter estimation of GTD model is proposed, and is applied in tomographic SAR imaging. By this method, complex scatter intensity can be estimated accurately.
This paper investigates biases of a parametric estimator of Doppler shift in mobile communications, and the result indicates that the conventional iterative autocorrelation function (ACF) estimator yields a significant estimation error at high mobile speeds. Therefore, a simple linear fitting is proposed to refine the Doppler shift estimation, in which only one fitting expression is required for different...
This paper concentrates on the identification problems of multi-input output-error autoregressive (OEAR) systems. By combining the auxiliary model identification idea with the filtering technique, we transform the multi-input OEAR system into two identification models and present a filtering based auxiliary model recursive least squares (F-AM-RLS) identification algorithm. Compared with the auxiliary...
The visual tracking of an object is a well-known problem, and it involves many fields of applications. Often a single sensor, the camera, could not provide enough information in order to track the whole object trajectory due to a low updating rate; therefore a multi-sensor system, based also on inertial measurements, could be necessary to improve the tracking accuracy. This leads to the fundamental...
Moving horizon estimation (MHE) has emerged as a popular technique for state estimation of nonlinear dynamical systems. A key parameter in MHE is the arrival cost that links the formulation in the current horizon with the past data. Most approaches available in literature use additional filters, such as extended Kalman filter or unscented Kalman filter to obtain the covariance matrix used in the arrival...
Improvement of software is the perspective line of seismic guard system development. Now it is possible to solve the problem of detection, classification, path parameter estimation by this software. But increasing system requirements lead to necessity of algorithm precision and operating speed increment. New object classification method in the seismic guard system is proposed in this paper. This method...
A frequency domain technique for optimal identification of linear discrete systems is presented. We consider the theoretical and applied aspects of the optimal parameter estimation, in the frequency domain, of the multidimensional stochastic linear discrete systems described by state space models. By an example of one model structure, the efficiency and sensibility is shown of applying the optimal...
The search of optimal input signals for the objects identification is an actual task and is considered in many publications. One of the directions is the Fisher information matrix using. The inputs optimal search technique on the basis of the Fisher information matrix using is offered. The technique essence consists in the analysis of the steady state. The state space model is considered when unknown...
Renewable energy systems have become very popular in recent years. The quality of power produced in such systems has to fulfill the requirements defined in the respective standards. The power control process is based on the frequency, amplitude and phase estimation of the grid signal. Recently, a fast and accurate estimation method was presented. The method is based on the FFT procedure and 3-point...
A decomposition based recursive least squares algorithm is derived for the identification of input nonlinear systems using the key term separation technique and the hierarchical identification principle. The proposed algorithm avoids estimating many crossed-parameters compared with the over-parameterization identification methods and requires less computational loads. Simulation results confirm the...
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