With the growing size of the system, this distributed Kalman filter (DKF) is widely used in multi-sensor networks. However, it is difficult for DKF to accurately estimate state values in non-Gaussian noise environment...
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With the growing size of the system, this distributed Kalman filter (DKF) is widely used in multi-sensor networks. However, it is difficult for DKF to accurately estimate state values in non-Gaussian noise environments. In this paper, a regression equation is first constructed to contain all sensor node information. Then, by bringing the minimum error entropy with fiducial points (MEEF) standard into the process of information fusion, a robust algorithm named centralized MEEF KF (CMEEF-KF) is presented, which is robust to non-Gaussian noise and unusual data. Furthermore, to overcome the communication burden of CMEEF-KF in sensor networks, the distributed MEEF-KF (DMEEF-KF) is developed, which construct a framework of consensus average method for node information fusion. Specifically, each sensor only exchanges the key information with its neighborhoods. In addition, in order to make the algorithm able to cope with the nonlinear state estimation problem, the distributed MEEF extended Kalman filter is also proposed. Eventually, the effectiveness of the suggested algorithms is demonstrated by land vehicle navigation and power system tracking state estimation using a 10-node sensor network.
minimum error entropy with fiducial points (MEEF) has received a lot of attention, due to its outstanding performance to curb the negative influence caused by non-Gaussian noises in the fields of machine learning and ...
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minimum error entropy with fiducial points (MEEF) has received a lot of attention, due to its outstanding performance to curb the negative influence caused by non-Gaussian noises in the fields of machine learning and signal processing. However, the estimate of the information potential of MEEF involves a double summation operator based on all available error samples, which can result in large computational burden in many practical scenarios. In this paper, an efficient quantization method is therefore adopted to represent the primary set of error samples with a smaller subset, generating a quantized MEEF (QMEEF). Some basic properties of QMEEF are presented and proved from theoretical perspectives. In addition, we have applied this new criterion to train a class of linear-in-parameters models, including the commonly used linear regression model, random vector functional link network, and broad learning system as special cases. Experimental results on various datasets are reported to demonstrate the desirable performance of the proposed methods to perform regression tasks with contaminated data.
As an outstanding forecasting-aided state estimation method for power systems, unscented Kalman filters (UKF) based on information theoretic criteria have been widely applied in recent years. In this paper, a robust U...
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As an outstanding forecasting-aided state estimation method for power systems, unscented Kalman filters (UKF) based on information theoretic criteria have been widely applied in recent years. In this paper, a robust UKF based on minimum error entropy with fiducial points utilizing generalized Versoria-Gaussian kernel (R-GVG-MEEF-UKF) is proposed to overcome non-Gaussian noise and outliers, sudden load changes, and bad measurement data. Specifically, the statistical linearization technique is applied to merge the measurement and state errors in the cost function and through fixed-point iteration to obtain the state estimate value. At the same time, to solve the problem of the influence of kernel shape coefficients, a framework for automatically searching for the optimal value of these coefficients is developed. In addition, the QR decomposition method is utilized to ensure the condition of the Cholesky decomposition. Finally, through IEEE-14,30,57 bus test systems, the numerical results have confirmed the high accuracy of the proposed algorithm compared with the existing algorithms.
High-order extend Kalman filtering (HEKF) is an excellent tool for addressing state estimation challenges in highly nonlinear systems under Gaussian noise conditions. However, HEKF may yield estimates with significant...
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High-order extend Kalman filtering (HEKF) is an excellent tool for addressing state estimation challenges in highly nonlinear systems under Gaussian noise conditions. However, HEKF may yield estimates with significant biases when the considered system is subjected to non-Gaussian disturbances. In response to this challenge, based on the minimum error entropy with fiducial points (MEEF), a novel HEKF (MEEFHEKF) is developed, and it exhibits robustness against intricate non-Gaussian noises. First, the nonlinear system is transformed into an augmented linear model through high-order Taylor approximation techniques. Subsequently, the development of the MEEFHEKF ensues through the resolution of an optimization problem grounded in the MEEF within the context of an augmented linear model. The MEEFHEKF, as put forward, operates as an online algorithm adopting a recursive structure, wherein the iterative equation is utilized to update the posterior estimates. Moreover, a sufficient condition is presented to confirm the existence and uniqueness of the fixed point in the iteration equation, guaranteeing the convergence of the introduced MEEFHEKF. Furthermore, an analysis of its computational complexity is also conducted to illustrate the computational burden. Finally, simulations substantiate the elevated precision in filtering and formidable robustness exhibited by the proposed algorithms when confronted with non-Gaussian disturbances.
This paper presents an innovative the minimum error entropy with fiducial points (MEEF)-based spline adaptive filtering (S-AF) algorithm, called SAF-MEEF algorithm, which outperforms the conventional SAF algorithms th...
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This paper presents an innovative the minimum error entropy with fiducial points (MEEF)-based spline adaptive filtering (S-AF) algorithm, called SAF-MEEF algorithm, which outperforms the conventional SAF algorithms that use the mean square error (MSE) criterion in reducing non-Gaussian interference. To overcome the limitation of the fixed step-size, a variable step-size strategy is also developed, resulting in the SAF-VMEEF algorithm, which improves the convergence speed and steady-state error performance. Furthermore, the computational complexity and convergence analysis of the SAF-MEEF are discussed. Nonlinear system identification simulations test the performance of the presented algorithms. Furthermore, this article accomplishes the application of nonlinear active noise control (ANC). Their effectiveness and robustness against non-Gaussian noise are demonstrated in different experimental scenarios, including a-stable noise, real-world functional magnetic resonance imaging (fMRI) noise, and real-life server room (SR) noise.
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