Huber’s M-Estimation-Based Cubature Kalman Filter for an INS/DVL Integrated System

Li, Yao; Hou, Lanhua; Yang, Yang; Tong, Jinwu

doi:10.1155/2020/1060672

Cited by 9 publications

(6 citation statements)

References 24 publications

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“…For the smaller τ, the Huber function has the characteristic of l 2 norm, so the estimation accuracy under Gaussian noise is guaranteed; for the larger τ, the Huber function grows slowly and can suppress the influence of outliers and noise. The Huber cost function combines the advantages of l 1 and l 2 norms, and its algorithm is more robust [16,17]. Defining Θ( e k+1,i ) =; ( e k+1,i ), the partial derivative of the residual vector can be found to determine the minimum value of the above formula:…”

Section: Hrukf Algorithm Derivationmentioning

confidence: 99%

“…Currently, the Huber method is often used in the CKF framework and is widely used in battery SOC, power grid system state estimation, combined INS/DVL navigation, and target tracking. However, it has not yet been used in distributed drive electric vehicle (EV) state parameter estimation [15][16][17][18]. In this study, the Huber method is introduced into the UKF vehicle state parameter estimation framework to correct for the measured noise and state covariance in real-time, which reduces the convergence time and improves the robustness and estimation accuracy of the algorithm.…”

Section: Introductionmentioning

confidence: 99%

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Huber-Based Robust Unscented Kalman Filter Distributed Drive Electric Vehicle State Observation

et al. 2021

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Accurate and real-time acquisition of vehicle state parameters is key to improving the performance of vehicle control systems. To improve the accuracy of state parameter estimation for distributed drive electric vehicles, an unscented Kalman filter (UKF) algorithm combined with the Huber method is proposed. In this paper, we introduce the nonlinear modified Dugoff tire model, build a nonlinear three-degrees-of-freedom time-varying parametric vehicle dynamics model, and extend the vehicle mass, the height of the center of gravity, and the yaw moment of inertia, which are significantly influenced by the driving state, into the vehicle state vector. The vehicle state parameter observer was designed using an unscented Kalman filter framework. The Huber cost function was introduced to correct the measured noise and state covariance in real-time to improve the robustness of the observer. The simulation verification of a double-lane change and straight-line driving conditions at constant speed was carried out using the Simulink/Carsim platform. The results show that observation using the Huber-based robust unscented Kalman filter (HRUKF) more realistically reflects the vehicle state in real-time, effectively suppresses the influence of abnormal error and noise, and obtains high observation accuracy.

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Section: Hrukf Algorithm Derivationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Huber-Based Robust Unscented Kalman Filter Distributed Drive Electric Vehicle State Observation

et al. 2021

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“…, w * n ), w i are the selected weights depending on M-estimation functions. In this case, coefficients are estimated using an adopted IRLS algorithm [27,[55][56][57] as follows:…”

Section: Institutional Review Board Statement: Not Applicablementioning

confidence: 99%

“…The common robust estimation method can be divided into several categories: M, MM, median, L1, Msplit, R, S, least-trimmed squares, and sign-constraint robust least squares estimation. Among these, Huber's M estimation has become one of the main robust estimation methods by virtue of its simple calculation and convenience to implement [27]. The key aspect is the involvement of a loss function that is applied to data errors that was selected to less rapidly increase than the square loss function that is used in least-squares or maximum-likelihood procedures.…”

Section: Introductionmentioning

confidence: 99%

Inverted Weibull Regression Models and Their Applications

Al-Dawsari

2021

Stats

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In this paper, we propose the classical and Bayesian regression models for use in conjunction with the inverted Weibull (IW) distribution; there are the inverted Weibull Regression model (IW-Reg) and inverted Weibull Bayesian regression model (IW-BReg). In the proposed models, we suggest the logarithm and identity link functions, while in the Bayesian approach, we use a gamma prior and two loss functions, namely zero-one and modified general entropy (MGE) loss functions. To deal with the outliers in the proposed models, we apply Huber and Tukey’s bisquare (biweight) functions. In addition, we use the iteratively reweighted least squares (IRLS) algorithm to estimate Bayesian regression coefficients. Further, we compare IW-Reg and IW-BReg using some performance criteria, such as Akaike’s information criterion (AIC), deviance (D), and mean squared error (MSE). Finally, we apply the some real datasets collected from Saudi Arabia with the corresponding explanatory variables to the theoretical findings. The Bayesian approach shows better performance compare to the classical approach in terms of the considered performance criteria.

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“…e computation of the robust estimation problem has been routinely done by means of an IRLS scheme: from early seminal works, [11], and early computing software packages, MIT ROSEPACK [12], until the most recent proposals, e.g., [13][14][15], including the general references on robust estimation, e.g., [6]. According to the IRLS scheme, in every iteration, the residuals of the previous iteration are used to form weights that are introduced in a new LS adjustment to obtain a new solution.…”

Section: Types and Properties Of Robust Estimatorsmentioning

confidence: 99%

Global Optimization of Redescending Robust Estimators

Baselga

Klein

Suraci

et al. 2021

Mathematical Problems in Engineering

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Robust estimation has proved to be a valuable alternative to the least squares estimator for the cases where the dataset is contaminated with outliers. Many robust estimators have been designed to be minimally affected by the outlying observations and produce a good fit for the majority of the data. Among them, the redescending estimators have demonstrated the best estimation capabilities. It is little known, however, that the success of a robust estimation method depends not only on the robust estimator used but also on the way the estimator is computed. In the present paper, we show that for complicated cases, the predominant method of computing the robust estimator by means of an iteratively reweighted least squares scheme may result in a local optimum of significantly lower quality than the global optimum attainable by means of a global optimization method. Further, the sequential use of the proposed global robust estimation proves to successfully solve the problem of M-split estimation, that is, the determination of parameters of different functional models implicit in the data.

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Huber’s M-Estimation-Based Cubature Kalman Filter for an INS/DVL Integrated System

Cited by 9 publications

References 24 publications

Huber-Based Robust Unscented Kalman Filter Distributed Drive Electric Vehicle State Observation

Huber-Based Robust Unscented Kalman Filter Distributed Drive Electric Vehicle State Observation

Inverted Weibull Regression Models and Their Applications

Global Optimization of Redescending Robust Estimators

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