| 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. |
| 关键词: Maximum correntropy criterion · Cubature Kalman filter · Non-Gaussian noise · Cauchy kernel · Gaussian kernel |
| DOI：https://doi.org/10.1007/s11768-022-00116-9 |
|
| 基金项目:Aastha Dak and Rahul Radhakrishnan have contributed equally to this work. |
|
| Non-iterative Cauchy kernel-based maximum correntropy cubature Kalman filter for non-Gaussian systems |
| Aastha Dak1,Rahul Radhakrishnan1 |
| (1Department of Electrical Engineering, Sardar Vallabhbhai National Institute of Technology, Surat, Gujarat 395007, India) |
| Abstract: |
| 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. |
| Key words: Maximum correntropy criterion · Cubature Kalman filter · Non-Gaussian noise · Cauchy kernel · Gaussian kernel |
