A study on input noise second-order filtering and smoothing of linear stochastic discrete systems with packet dropouts

Zhao, Haiquan; Li, Zhifang; Li, Bin; Li, Tongxing

doi:10.1186/s13662-020-02903-7

Cited by 4 publications

(3 citation statements)

References 30 publications

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“…The classic estimation algorithms based on RLMVE (RLMVE‐based) include Kalman filter, 7,8 extended Kalman filter (EKF), 9,10 unscented Kalman filter (UKF), 11‐13 cubature Kalman filter (CKF) 14,15 . However, the RLMVE‐based estimation algorithms require accurate system parameters (noise statistics and model parameters) 16,17 . In practical applications, the estimation performance may be severely degraded, because of modeling errors, random interference, and so on.…”

Section: Introductionmentioning

confidence: 99%

Robust estimation algorithm based on prior probability statistics

Wang

Hao

2021

Intl J Robust & Nonlinear

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For the nonlinear systems with uncertain system parameters, based on the recursive linear minimum variance estimation framework, the effect of the system parameters on the mean squared error (MSE) is analyzed, that is, the greater the system parameters deviation (whether they are positive or negative deviation) is, the greater the MSE is. Based on the analysis results and prior probability statistics (probability distributions or sampling distributions), an innovative robust estimation algorithm is proposed for nonlinear systems with uncertain system parameters. Three examples verify the effectiveness of the proposed algorithm.

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Section: Introductionmentioning

confidence: 99%

Robust estimation algorithm based on prior probability statistics

Wang

Hao

2021

Intl J Robust & Nonlinear

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“…For linear discrete-time non-Gaussian systems, according to the polynomial filtering theory, a solution to the quadratic estimation problem of non-Gaussian noise was given in [9]. H. Zhao and Z. Li presented a novel Kalman-like nonlinear non-Gaussian noise estimation method based on the packet dropout probability distribution and polynomial filtering technique [10]. W. Liu and Z. Deng solved the design problem of robust white noise deconvolution estimators for a class of uncertain systems with missing measurements, uncertain noise variances, and linearly correlated white noises [11].…”

Section: Introductionmentioning

confidence: 99%

Second-Order EKF White Noise Estimator Design for Hybrid Systems

et al. 2021

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The extended Kalman filter (EKF) has a wide range of applications (especially in power battery management systems) with a rapidly increasing market share. It aims to minimize the symmetric loss function (mean square error) and it has high accuracy and efficiency in battery state estimation. This study deals with the second-order extended Kalman filter-based process and the measurement white noise estimation problem for nonlinear continuous-discrete systems. The design of the white noise filter and smoother were, firstly, converted into a linear estimation problem by the second-order Taylor series expansion approximation and the function that makes the second-order term approximately equivalent to the estimation error variance. Secondly, based on the projection formula of the Kalman filtering (KF) theory and the Lemma of expectation for quadratic and quartic product traces of random vectors, the second-order EKF was derived. Then, to generate white noise estimators in the forms of filtering and smoothing, we derived a recursive solution, using an innovation method. Finally, a numerical example is given to show the effectiveness of the proposed method.

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“…In the last decades some authors have been interested in finding some discrete results on l p (N)analogues to L p (R)-bounds in different fields in analysis and as a result this subject becomes a topic of ongoing researches. One reason for this upsurge of interest in discrete case is also due to the fact that discrete operators may even behave differently from their continuous counterparts (see [17,36]). So it is natural to look for the discrete versions of the inequalities (1.7) and (1.8) which is one of our aims.…”

Section: Introductionmentioning

confidence: 99%