Maximum Correntropy Criterion Constrained Kalman Filter

Fakoorian, Seyed Abolfazl; Moosavi, Mahmoud; Izanloo, Reza; Azimi, Vahid; Simon, Dan

doi:10.1115/dscc2017-5326

Cited by 8 publications

(3 citation statements)

References 20 publications

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“…In view of this fact, the filters developed under the MCC strategy were shown to improve the estimator robustness against outliers or impulsive noises and, hence, they are of special interest when estimating stochastic systems with heavy‐tailed uncertainties . In particular, the comparative study presented in demonstrates that the MCC KF‐like strategy outperforms the classical KF as well as some nonlinear Bayesian filters for estimation quality in the presence of non‐Gaussian uncertainties in state‐space model.…”

Section: Introductionmentioning

confidence: 98%

Sequential Maximum Correntropy Kalman Filtering

Kulikova

2018

Asian Journal of Control

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This paper explores a linear state estimation problem in non-Gaussian setting and suggests a computationally simple estimator based on the maximum correntropy criterion Kalman filter (MCC-KF). The first MCC-KF method was developed in Joseph stabilized form. It requires two n × n and one m × m matrix inversions, where n is a dimension of unknown dynamic state to be estimated, and m is a dimension of available measurement vector. Therefore, the estimator becomes impractical when the system dimensions increase. Our previous work has suggested an improved MCC-KF estimator (IMCC-KF) and its factored-from (square-root) implementations that enhance the MCC-KF estimation quality and numerical robustness against roundoff errors. However, the proposed IMCC-KF and its square-root implementations still require the m × m matrix inversion in each iteration step of the filter. For numerical stability and computational complexity reasons it is preferable to avoid the matrix inversion operation. In this paper, we suggest a new IMCC-KF algorithm that is more accurate and computationally cheaper than the original MCC-KF and previously suggested IMCC-KF. Furthermore, compared with stable square-root algorithms, the new method is also accurate, but less computationally expensive. The results of numerical experiments substantiate the mentioned properties of the new estimator on numerical examples.

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Section: Introductionmentioning

confidence: 98%

Sequential Maximum Correntropy Kalman Filtering

Kulikova

2018

Asian Journal of Control

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“…In recent years, filters under MCC have attracted increasing interests [19][20][21]. Existing research work shows that the filter under MCC has better performance in handling stochastic systems with non-Gaussian uncertainties [22][23][24]. Unlike the existing online identification techniques based on the Kalman filter which use the second-order moment of the prediction errors, the proposed method makes use of higher order moments.…”

Section: Introductionmentioning

confidence: 99%

Online identification of aerodynamics with fast time‐varying features using Kalman filter

Zhang

Zhu

2020

IET Control Theory & Appl

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This paper investigates the online identification method of the unsteady aerodynamics in the post-stall manoeuvres. The main unsteady features are hysteresis and fast time varying due to the complex flow structure at large angles of attack. When limited test data are available, the data-driven model cannot be identified accurately, especially in dealing with the fast time-varying features, so online identification is required. A constant acceleration model-based Kalman filter is constructed by taking into account the unsteady item in the aerodynamic model. Unlike the previous work which only uses the information of second-order moments of errors, the proposed method uses the maximum correntropy criterion. Simulation studies according to the wind tunnel test data show that the proposed method can effectively track the fast time-varying features and estimate the aerodynamic coefficients with high accuracy. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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“…In order to effectively work out the estimation problem when the system has colored Gaussian noise, several optimization criteria have been proposed to replace the MMSE. It is worth noting that the maximum correntropy criterion (MCC) has been successfully used in some applications mixed together with colored Gaussian noises [19][20][21].…”

Section: Introductionmentioning

confidence: 99%

MCC-CKF: A Distance Constrained Kalman Filter Method for Indoor TOA Localization Applications

et al. 2019

Electronics

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Non-Gaussian noise may have a negative impact on the performance of the Kalman filter (KF), due to its adoption of only second-order statistical information. Thus, KF is not first priority in applications with non-Gaussian noises. The indoor positioning based on arrival of time (TOA) has large errors caused by multipath and non-line of sight (NLOS). This paper introduces the inequality state constraint to enhance the ranging performance. Based on these considerations, we propose a constrained Kalman filter based on the maximum correntropy criterion (MCC-CKF) to enhance the TOA performance in the extreme environment of multipath and non-line of sight. Pratical experimental results indicate that MCC-CKF outperforms other estimators, such as Kalman filter and Kalman filter based on maximum entropy.

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Maximum Correntropy Criterion Constrained Kalman Filter

Cited by 8 publications

References 20 publications

Sequential Maximum Correntropy Kalman Filtering

Sequential Maximum Correntropy Kalman Filtering

Online identification of aerodynamics with fast time‐varying features using Kalman filter

MCC-CKF: A Distance Constrained Kalman Filter Method for Indoor TOA Localization Applications

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