A distributed maximum correntropy Kalman filter

Wang, Gang; Xue, Rui; Wang, Jinxin

doi:10.1016/j.sigpro.2019.02.030

Cited by 59 publications

(21 citation statements)

References 19 publications

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“…At present, there is an increasing number of researchers that recognize the importance of the DF problem over SNs (Bu et al, 2019(Bu et al, , 2018He et al, 2020;Liu et al, 2019;Wang et al, 2020;Yang et al, 2019). Some typical research directions include that the distributed H ∞ filtering (Dong et al, 2013;Han, Wang, Chen et al, 2021;Qu et al, 2019;Shen et al, 2010Shen et al, , 2011, and the distributed Kalman filtering (Ji et al, 2017;Li, Dong et al, 2019;Wang et al, 2019;Wu et al, 2018;Yang et al, 2020), and distributed set-membership filtering (Liu et al, 2019;Ma et al, 2017). As for DF problems, each node has its own filter through the available information from both itself and its neighbouring sensor nodes.…”

Section: Introductionmentioning

confidence: 99%

Event-triggered distributed filtering for discrete-time systems with integral measurements

Cong

Han

et al. 2021

Systems Science & Control Engineering

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This paper handles the event-triggered distributed filtering problem for a class of discrete-time systems with integral measurements over sensor networks (SNs). First of all, a plant is monitored by an SN formulated by a digraph. The integral measurement phenomenon is modelled for each node to account for the interval time taken by sample collection. Next, to mitigate the communication burden of network, an event generator function is introduced for each node to regulate the data transmission to its neighbouring nodes. This paper aims to find the suitable filter gains for each node such that the filtering error dynamics satisfies the exponentially ultimately boundedness. Via the Lyapunov stability theory, a sufficient condition is established which ensures the existence of the distributed filter under the desirable performance index. Moreover, the ultimate bound in the performance index is minimized via the robust optimization method. Then, the filter gains are acquired by solving a linear matrix inequality with the YALMIP toolbox. Finally, a numerical simulation example is employed to verify the usefulness of the distributed filtering algorithm developed in this paper.

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Section: Introductionmentioning

confidence: 99%

Event-triggered distributed filtering for discrete-time systems with integral measurements

Cong

Han

et al. 2021

Systems Science & Control Engineering

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“…Nevertheless, PF has some inherent practical problems, such as complexity of calculation, selection strategy of importance function and so on. In the field of information theoretic learning, maximum correntropy (MC) criterion has been successfully utilized for the non-Gaussian signal processing problems [28][29][30][31][32][33][34][35][36][37][38][43][44][45][46]. Under the MC criterion, several effective filter design methods were also developed for the non-Gaussian systems.…”

Section: Introductionmentioning

confidence: 99%

“…36) Then the posterior state estimate and the posterior estimation error covariance matrix are obtained by…”

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confidence: 99%

Nonlinear Non-Gaussian Estimation Using Maximum Correntropy Square Root Cubature Information Filtering

Feng

Zhou

et al. 2020

IEEE Access

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This paper concerns the nonlinear filter designing methods in the information space of the nonlinear systems with non-Gaussian noises. Firstly, the prediction information vector is obtained by the traditional square root cubature information filtering algorithm. Then, under the maximum correntropy criterion, the prediction information vector is corrected with the contribution information vector obtained by the non-Gaussian measurement. The information filtering gain is obtained by utilizing the state information correntropy matrix and the measurement information correntropy matrix, in which, the state prediction is taken as the state value. In order to improve the advantage of the above nonlinear non-Gaussian information filter in filtering accuracy, with the help of fixed-point theory, an iterative computation method is further developed to update the estimation information vector and the state estimate. The effectiveness of the two proposed nonlinear non-Gaussian filtering methods is illustrated in final four simulation examples. INDEX TERMS Maximum correntropy criterion; information correntropy matrix; square root cubature information filter;

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“…In [21], the fixed-point theory is used for the identification of Wiener systems, which includes an infinite impulse response (IIR) system and a nonlinear static function. In [22,23], the maximum correntropy Kalman filter is designed by solving a fixed-point equation. The fixed-point iteration Gaussian sum filtering estimator with unknown time-varying non-Gaussian measurement noise is proposed in [24].…”

Section: Introductionmentioning

confidence: 99%

An EKF-Based Fixed-Point Iterative Filter for Nonlinear Systems

Feng

Wen

2019

Sensors

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In this paper, a fixed-point iterative filter developed from the classical extended Kalman filter (EKF) was proposed for general nonlinear systems. As a nonlinear filter developed from EKF, the state estimate was obtained by applying the Kalman filter to the linearized system by discarding the higher-order Taylor series items of the original nonlinear system. In order to reduce the influence of the discarded higher-order Taylor series items and improve the filtering accuracy of the obtained state estimate of the steady-state EKF, a fixed-point function was solved though a nested iterative method, which resulted in a fixed-point iterative filter. The convergence of the fixed-point function is also discussed, which provided the existing conditions of the fixed-point iterative filter. Then, Steffensen’s iterative method is presented to accelerate the solution of the fixed-point function. The final simulation is provided to illustrate the feasibility and the effectiveness of the proposed nonlinear filtering method.

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A distributed maximum correntropy Kalman filter

Cited by 59 publications

References 19 publications

Event-triggered distributed filtering for discrete-time systems with integral measurements

Event-triggered distributed filtering for discrete-time systems with integral measurements

Nonlinear Non-Gaussian Estimation Using Maximum Correntropy Square Root Cubature Information Filtering

An EKF-Based Fixed-Point Iterative Filter for Nonlinear Systems

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