Maximum correntropy unscented filter

Liu, Xi; Chen, Badong; Xu, Bin; Wu, Zongze; Honeiné, Paul

doi:10.1080/00207721.2016.1277407

Cited by 108 publications

(56 citation statements)

References 34 publications

(23 reference statements)

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“…Most existing works have addressed the problem under a specific scenario, but the resulting methods may degrade significantly under a different scenario. For example, the method in [27] was developed under the assumption that the process and measurement are both contaminated, its performance may experience considerable degradation under a different scenario, e.g., when only the process or the measurement is contaminated. In addition, most recently developed robust Kalman filtering schemes using the MCC were based on linear regression [37,38,27,39], where a linearization procedure is inevitable, resulting in a loss of accuracy.…”

Section: Related Workmentioning

confidence: 99%

“…Numerical examples are presented in this section to illustrate the performance of the proposed RCKF and RCKS in the presence of heavy-tailed noises. We also compare the state estimates of the proposed algorithms with those of the conventional CKF [4], conventional CKS [43], linear regression and MCC based robust Kalman filter (LRKF) [27], nonlinear regression based Huber Kalman filter (HRKF) [16] and variational Bayesian based student's cubature Kalman smoother (TCKS) [21]. The LRKF is originally proposed in the unscented Kalman filter (UKF) framework and here for consistency with the other compared algorithms, we set parameters (α, β, κ) = (1, 0, 0) in LRKF, which makes it functionally equivalent to a CKF.…”

Section: Numerical Simulationsmentioning

confidence: 99%

“…Recently a concept called correntropy in information theoretic learning and an associated maximum correntropy criterion (MCC) [26] was utilized as a new cost function for robust filtering [27]. As shown in [26], MCC has a close relationship with M-estimators.…”

Section: Introductionmentioning

confidence: 99%

“…A major one is that the methods ignore covariance propagation, and hence some important statistic information about the state may get lost. Besides, these methods were primarily designed for smoothing, and it is difficult to extend them for filtering, which is more frequently encountered in real-time applications.Recently a concept called correntropy in information theoretic learning and an associated maximum correntropy criterion (MCC) [26] was utilized as a new cost function for robust filtering [27]. As shown in [26], MCC has a close relationship with M-estimators.…”

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confidence: 99%

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Maximum Correntropy Derivative-Free Robust Kalman Filter and Smoother

Wang

Zhang

et al. 2018

IEEE Access

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We consider the problem of robust estimation involving filtering and smoothing for nonlinear state space models which are disturbed by heavy-tailed impulsive noises. To deal with heavy-tailed noises and improve the robustness of the traditional nonlinear Gaussian Kalman filter and smoother, we propose in this work a general framework of robust filtering and smoothing, which adopts a new maximum correntropy criterion to replace the minimum mean square error for state estimation. To facilitate understanding, we present our robust framework in conjunction with the cubature Kalman filter and smoother. A half-quadratic optimization method is utilized to solve the formulated robust estimation problems, which leads to a new maximum correntropy derivative-free robust Kalman filter and smoother. Simulation results show that the proposed methods achieve a substantial performance improvement over the conventional and existing robust ones with slight computational time increase. IntroductionIn recent years, estimation problems involving filtering and smoothing based on dynamic state space models (SSMs) have received significant attentions. These problems are frequently encountered in many areas, such as target tracking, fault detection and diagnosis, parameter estimation, navigation, and many others. The celebrated Kalman filter (KF) [1] offers optimal estimation with the minimum mean square error (MMSE) for a linear SSM when both the process and measurement noises are Gaussian. Nevertheless, most applications in practice inherently have nonlinear SSMs, for which an optimal nonlinear filter or smoother is typically intractable. Several sub-optimal solutions were proposed for nonlinear SSMs, including the extended KF [2], unscented KF [3], cubature Kalman filter (CKF) [4], and others. Some interesting relations among these solutions can be found in [5,6]. Meanwhile, for smoothing, the Rauch-Tung-Striebel (RTS) smoother was introduced in [7] for linear Gaussian SSMs. A general framework of Gaussian optimal smoothing for nonlinear SSMs was proposed in [8], following which several sub-optimal nonlinear RTS smoothers were developed, such as the extended RTS smoother, unscented RTS smoother, cubature Kalman smoother (CKS), and others.Although the aforementioned filtering and smoothing methods in general perform well under the Gaussian assumption, they can potentially break down in the presence of heavy-tailed non-Gaussian noises, which may appear in either the process procedure or measurement procedure. This happens in, for instance, tracking a maneuvering target with outliers in observations [9]. A primary reason behind the degradation is that the traditional KF/RTS and their sub-optimal extensions were derived from the MMSE criterion, and the resulting estimates are sensitive to heavy-tailed noises [10]. * H. Wang is with the Persistent efforts have been devoted to tackling heavy-tailed noises in order to obtain more robust state estimates. Multiple models based techniques and sequential Monte Carlo sampling methods can be used ...

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Section: Related Workmentioning

confidence: 99%

Section: Numerical Simulationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Maximum Correntropy Derivative-Free Robust Kalman Filter and Smoother

Wang

Zhang

et al. 2018

IEEE Access

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“…Similar to the original UKF, the new filter also has a recursive structure and is suitable for online implementation. It is worth noting that the proposed MCUKF is different from the algorithm in [ 33 ].…”

Section: Introductionmentioning

confidence: 99%

Maximum Correntropy Unscented Kalman Filter for Spacecraft Relative State Estimation

Liu

Zhao

et al. 2016

Sensors

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A new algorithm called maximum correntropy unscented Kalman filter (MCUKF) is proposed and applied to relative state estimation in space communication networks. As is well known, the unscented Kalman filter (UKF) provides an efficient tool to solve the non-linear state estimate problem. However, the UKF usually plays well in Gaussian noises. Its performance may deteriorate substantially in the presence of non-Gaussian noises, especially when the measurements are disturbed by some heavy-tailed impulsive noises. By making use of the maximum correntropy criterion (MCC), the proposed algorithm can enhance the robustness of UKF against impulsive noises. In the MCUKF, the unscented transformation (UT) is applied to obtain a predicted state estimation and covariance matrix, and a nonlinear regression method with the MCC cost is then used to reformulate the measurement information. Finally, the UT is adopted to the measurement equation to obtain the filter state and covariance matrix. Illustrative examples demonstrate the superior performance of the new algorithm.

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Distributed maximum correntropy unscented Kalman filter under hybrid attacks in non‐Gaussian environment

Liu,

Ma,

Huang

et al. 2024

Asian Journal of Control

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This paper addresses a distributed nonlinear filtering issue based on maximum correntropy for dealing with randomly occurring hybrid cyber‐attacks in non‐Gaussian environment. The types of cyber‐attacks include denial of service attacks and deception attacks. First, a modified distributed unscented Kalman filter is proposed, using Cauchy kernel‐based maximum correntropy criterion instead of the traditional mean square error criterion, against cyber‐attacks and non‐Gaussian noise. Then based on fixed‐point iterative rules and information fusion strategy, the update equations of state estimates and covariance matrices of the proposed filter are obtained. Furthermore, the sufficient condition guaranteeing its convergence and computational complexity are also given. Finally, an illustrative example is presented to verify the effectiveness of the proposed filter under hybrid cyber‐attacks in non‐Gaussian environment.

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Maximum correntropy unscented filter

Cited by 108 publications

References 34 publications

Maximum Correntropy Derivative-Free Robust Kalman Filter and Smoother

Maximum Correntropy Derivative-Free Robust Kalman Filter and Smoother

Maximum Correntropy Unscented Kalman Filter for Spacecraft Relative State Estimation

Distributed maximum correntropy unscented Kalman filter under hybrid attacks in non‐Gaussian environment

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