典型文献
Non-iterative Cauchy kernel-based maximum correntropy cubature Kalman filter for non-Gaussian systems
文献摘要：
This article addresses the nonlinear state estimation problem where the conventional Gaussian assumption is completely relaxed.Here,the uncertainties in process and measurements are assumed non-Gaussian,such that the maximum correntropy criterion(MCC)is chosen to replace the conventional minimum mean square error criterion.Furthermore,the MCC is realized using Gaussian as well as Cauchy kernels by defining an appropriate cost function.Simulation results demonstrate the superior estimation accuracy of the developed estimators for two nonlinear estimation problems.
文献关键词：
中图分类号：
作者姓名：
Aastha Dak;Rahul Radhakrishnan
作者机构：
Department of Electrical Engineering,Sardar Vallabhbhai National Institute of Technology,Surat,Gujarat 395007,India
文献出处：
引用格式：
[1]Aastha Dak;Rahul Radhakrishnan-.Non-iterative Cauchy kernel-based maximum correntropy cubature Kalman filter for non-Gaussian systems)[J].控制理论与技术（英文版）,2022(04):465-474
A类：
B类：
Non,iterative,Cauchy,maximum,correntropy,cubature,Kalman,filter,Gaussian,systems,This,article,addresses,nonlinear,state,estimation,where,conventional,assumption,completely,relaxed,Here,uncertainties,process,measurements,assumed,such,that,criterion,MCC,chosen,replace,minimum,mean,square,error,Furthermore,realized,using,well,kernels,by,defining,appropriate,cost,function,Simulation,results,demonstrate,superior,accuracy,developed,estimators,two,problems
AB值：
0.659987
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