Maximum correntropy square-root cubature Kalman filter with application to SINS/GPS integrated systems

Liu, Xi; Qu, Hua; Zhao, Jihong; Yue, Pengcheng

doi:10.1016/j.isatra.2018.05.001

Cited by 112 publications

(69 citation statements)

References 31 publications

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“…In the process of noise processing, considering the sudden occurrence of the system in the middle (the w 0 weight is reset in the simulation), the unknown system vector w 0 changes to −w 0 at iteration 2 × 10 4 . GSNR = 10.47 dB from (25). The step size and the performance of the algorithm are as shown in Figs 2 and 3, respectively.…”

Section: The System Identification Simulation Resultsmentioning

confidence: 99%

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A Variable Step Size for Maximum Correntropy Criterion Algorithm with Improved Variable Kernel Width

Wang

Gao

et al. 2020

IEEJ Transactions Elec Engng

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The maximum correntropy criterion (MCC) algorithm has popularly been used in suppressing impulsive noise. It mainly relies on the choice of step size and kernel width. With the study of step size, this paper proposes a novel algorithm that updates the step size based on constructing a variable step size function that uses the moving weighted average algorithm to keep performance stable and sigmoid function to speed the convergence rate. The proposed algorithm is robust against impulsive noises and generates a large step size to accelerate the convergence in iteration beginning, and system mutation makes the system identification performance more stable when the error is small. Based on the improved variable kernel width maximum entropy criterion (IVKW‐MCC) algorithm, which overcomes the shortcoming of how to choose a reliable kernel width in the MCC algorithm, simulation results are compared with other MCC algorithms under impulsive noise interference. Simulations in the system identification scenarios show that the proposed algorithm has better performance in improving the convergence speed over other algorithms. © 2020 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

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Section: The System Identification Simulation Resultsmentioning

confidence: 99%

“…T represents the input signal vector with symbol T being transposed, w 0 stands for the impulsive response of the unknown system, M denotes the length of the vector, and η(k ) is the impulsive background noise signal with zero mean and variance δ 2 [25]. The error is given by…”

Section: Review Of the Ivkw-mcc Algorithmmentioning

confidence: 99%

A Variable Step Size for Maximum Correntropy Criterion Algorithm with Improved Variable Kernel Width

Wang

Gao

et al. 2020

IEEJ Transactions Elec Engng

View full text Add to dashboard Cite

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“…If this covariance matrix is negative, the filtering process will be diverged. This problem can be overcome by either selecting an appropriate initial state value [10] or using the matrix decomposition technique such as singular value decomposition [33] and square root decomposition [34].…”

Section: Journal Of Sensorsmentioning

confidence: 99%

“…If the nonlinear system model involves error, the CKF residual vectors e k and ε k given by (34) and (35) will be biased, and their magnitudes will also increase. However, as shown in (36) and (37), RWCKF enables us to reduce the magnitudes of the residual vectors e * k and ε * k by adjusting randomly weighted factors β i ði = 1, 2, ⋯, mÞ to restrain system model error's disturbance, leading to increased estimation accuracy.…”

Section: Journal Of Sensorsmentioning

confidence: 99%

Randomly Weighted CKF for Multisensor Integrated Systems

et al. 2019

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The cubature Kalman filter (CKF) is an estimation method for nonlinear Gaussian systems. However, its filtering solution is affected by system error, leading to biased or diverged system state estimation. This paper proposes a randomly weighted CKF (RWCKF) to handle the CKF limitation. This method incorporates random weights in CKF to restrain system error’s influence on system state estimation by dynamic modification of cubature point weights. Randomly weighted theories are established to estimate predicted system state and system measurement as well as their covariances. Simulation and experimental results as well as comparison analyses demonstrate the presented RWCKF conquers the CKF problem, leading to enhanced accuracy for system state estimation.

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“…Recently, information‐theoretic learning (ITL) has enlightened some new optimization criteria, such as the maximum correntropy criterion (MCC), which uses the information‐theoretic quantities directly estimated from data as the optimization cost function, and has been successfully applied in many areas in impulsive‐noise environments . ITL costs can capture higher‐order statistics of data and stay intensive to large outliers, thus offering better performance than conventional second‐order statistical measures, such as variance and mean square error.…”

Section: Introductionmentioning

confidence: 99%

A robust blind adaptive multiuser detection based on maximum correntropy criterion in satellite CDMA systems

Yue

Zhao

et al. 2019

Trans Emerging Tel Tech

Self Cite

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Satellite code‐division multiple‐access systems use multiuser detection (MUD) methods to deal with multiple‐access interferences. Among the different kinds of MUD, blind adaptive MUD regarding the system as a state‐space model is widely used for its tracking capability and independence of training sequences. The innovation process noise of the state‐space model is usually assumed to follow the Gaussian distribution, and the minimum mean‐square error criterion is the default criterion for most conventional blind adaptive MUD methods. In the past few years, the impulsive noise of the innovation process has attracted attention, declaring that a Gaussian distribution is not suitable for simulating the impulsive noise. Thus, more criteria are proposed to enhance the robustness of the blind adaptive MUD. This paper improves the robustness of MUD methods under impulsive noise by bringing forward a new cost function based on the maximum correntropy criterion and deduces the expression of the blind adaptive detector. Simulation results show that the proposed method achieves superior performance in terms of bit error rate, convergence, and robustness compared with other methods.

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Maximum correntropy square-root cubature Kalman filter with application to SINS/GPS integrated systems

Cited by 112 publications

References 31 publications

A Variable Step Size for Maximum Correntropy Criterion Algorithm with Improved Variable Kernel Width

A Variable Step Size for Maximum Correntropy Criterion Algorithm with Improved Variable Kernel Width

Randomly Weighted CKF for Multisensor Integrated Systems

A robust blind adaptive multiuser detection based on maximum correntropy criterion in satellite CDMA systems

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