A Framework of Cubature- Hᵢ/H ∞ -Fault Detection and Robust H-Infinity Kalman Filter of Ship SINS/GNSS Integrated System

Yang

IEEE Trans. Instrum. Meas.

et al. 2022

Due to the unfavorable interference of non-Gaussian noise, abnormal system states, and rough measurement errors, dynamic state estimation (DSE) plays an important role in the safe operation of power system. A novel DSE method based on an adaptive cubature Kalman filter (CKF) with generalized correntropy loss (GCL) criterion, termed AGCLCKF, is developed to deal with the complex non-Gaussian distribution noises of power system in this paper. First, a nonlinear regression model is derived to simultaneously incorporate the state and noise errors into the GCL cost function, and a fixed-point iteration is exploited to recursively update the posterior state estimate. Then, considering that the filtering performance of the estimator is largely determined by the kernel bandwidth in correntropy, an adaptive factor is established to adjust the kernel bandwidth of kernel function in real-time, which can improve the flexibility and accuracy of dynamic state estimation in the existence of bad measurement information. Finally, extensive simulation results carried out on the IEEE 39-bus test system demonstrate that the proposed method can achieve much accuracy and robustness under various situations.

Section: A Case 1: Gaussian Noise Distribution Conditionsmentioning

confidence: 93%

“…where MC N , S N and T N represent the total number of Monte Carlo runs, state variables and simulation time, respectively; In order to verify and highlight the efficacy of the proposed method, the methods CKF [11], HCKF [35], and the developed GCLCKF are utilized to make comparisons under the different circumstances.…”

Section: Illustrative Examplementioning

confidence: 99%

Robust Dynamic State Estimation for Power System Based on Adaptive Cubature Kalman Filter With Generalized Correntropy Loss

Yang

IEEE Trans. Instrum. Meas.

et al. 2022

“…Step 3: Two coefficient matrices are calculated using (20) and ( 21), and the cubature point error is calculated using (18). Finally, the new error is obtained using (23). The difference between the CKF and the ICKF is shown in figure 1 in red.…”

Section: Calculate Process For Ickfmentioning

confidence: 99%

“…Different adaptive methods were used to improve the performance of the filter. These adaptive factors can be generated by a method based on either the sensor measurement noise variance or complex mathematical methods, such as the H-infinity strategy [23], M-estimation theory [24], and maximum likelihood [25]. Unfortunately, these methods either suffer from a low rank in the matrix or involve human experience, which makes them difficult to apply in practical scenarios.…”

Section: Introductionmentioning

confidence: 99%

An improved GNSS/INS navigation method based on cubature Kalman filter for occluded environment

Liu¹,

Shi²,

Liu³

et al. 2022

Meas. Sci. Technol.

GNSS/INS (Global Navigation Satellite System and Inertial Navigation System) based on CKF (Cubature Kalman Filter) can provide an effective land vehicle navigation and positioning solution. The accuracy of CKF depends mainly on the prior Probability Density Function (PDF), which is inaccurate when the GNSS signal is temporarily blocked in an urban environment. Although the ICKF (Improved Cubature Kalman Filter) can reduce cubature point errors by using the historical information obtained iteratively, the generation of cubature points is still related to measurement noise. To obtain a more accurate PDF, adjustment off the sensor measurement noise is necessary. In this paper, an adaptive improved cubature Kalman filter for GNSS/INS system based on the Mahalanobis distance called MD-ICKF is proposed, which expands the usability of the micro-electronic mechanical system (MEMS) based integrated navigation system in an urban environment. Two lower triangular matrices obtained by Cholesky decomposition are used to adjust the cubature point errors during each iteration. Furthermore, the measurement noise will be inflated to obtain a more accurate PDF when it exceeds the threshold calculated by the Mahalanobis distance and Chi-square test. The field experiment results show that the proposed method has better positioning accuracy and performance than CKF and ICKF in an occluded environment

“…An approximately optimal H∞ filter was found by constantly adjusting the parameter γ during target tracking [11]. A method to adjust γ to its minimum at each iteration in the framework of unscented transformation was presented in [12,18], and a similar strategy was used in [19][20][21] to generate adaptive H∞ Kalman filters. An optimal robust H∞ estimator was obtained by minimising the H∞ norm from uncertain disturbances to estimation errors by using the convex optimisation method [22].…”

Section: Introductionmentioning

confidence: 99%

Singular value decomposition‐based iterative robust cubature Kalman filtering and its application for integrated global positioning system/strapdown inertial navigation system navigation

Zhang

et al. 2021

IET Radar Sonar & Navi

The issue of non-linear robust state estimation in the integration of a strapdown inertial navigation system and global positioning system is addressed in this study. Based on the cubature Kalman filtering frame, a non-linear robust filter called a robust cubature Kalman filter (RCKF) was introduced to address the outliers and the inaccurate model. It has been found that the determination of an optimal restriction parameter is crucial for maintaining the robustness and accuracy of the non-linear robust filter. Unfortunately, the value of this restriction parameter is always determined by experience. In this study, an iterative strategy was proposed to adaptively attain the optimal restriction parameter without much previous experience. To improve the computational stability of the iterative non-linear robust filter, a singular value decomposition strategy was adopted simultaneously. Two case studies indicate that the iterative RCKF can achieve greater robustness and accuracy using the methodology discussed in this study. K E Y W O R D S cubature Kalman filter, integrated navigation system, non-linear filter, singular value decompositionThis is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.