Adaptive unscented Kalman filter for parameter and state estimation of nonlinear high-speed objects

Deng, Fang; Chen, Jie; Chen, Chen

doi:10.1109/jsee.2013.00076

Cited by 38 publications

(29 citation statements)

References 33 publications

(44 reference statements)

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“…And then, combining the density function given in Equation (17), Equation (18) and Equation (19), under the maximization of ( ) J k , the k Q and k R given as:…”

Section: Map Criterionmentioning

confidence: 99%

“…Deng.et al in [18]proposed an adaptive unscented Kalman filter (AUKF) to estimate the time-varying parameters and states of a kind of nonlinear high-speed objects, which a strong tracking filter and wavelet transform were employed to improve the robustness of unscented Kalman filter (UKF) under the process noise is inaccuracy, and the estimate accuracy by the variance of measurement noise, respectively. In [19], an adaptive unscented Kalman filter algorithm with dynamic thresholds of covariance used to update measurement noise covariance matrix real-time was developed for satellite fault detection and diagnosis by the authors.…”

Section: Introductionmentioning

confidence: 99%

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A Hybrid Adaptive Unscented Kalman Filter Algorithm

Chen

Zhang

et al. 2017

Preprint

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Abstract:In order to overcome the limitation of the traditional adaptive Unscented Kalman Filtering (UKF) algorithm in noise covariance estimation for statement and measurement, we propose a hybrid adaptive UKF algorithm based on combining Maximum a posteriori (MAP) criterion and Maximum likelihood (ML) criterion, in this paper. First, to prevent the actual noise covariance deviating from the true value which can lead to the state estimation error and arouse the filtering divergence, a real-time covariance matrices estimation algorithm based on hybrid MAP and ML is proposed for obtaining the statement and measurement noises covariance, respectively; and then, a balance equation the two kinds of covariance matrix is structured in this proposed to minimize the statement estimation error. Compared with the UFK based MAP and based ML, the proposed algorithm provides better convergence and stability.

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“…And then, combining the density function given in Equation (17), Equation (18) and Equation (19), under the maximization of ( ) J k , the k Q and k R given as:…”

Section: Map Criterionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Hybrid Adaptive Unscented Kalman Filter Algorithm

Chen

Zhang

et al. 2017

Preprint

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“…The conventional Kalman filtering is using the LPC coefficient to estimate the observations of the speech signal [6]. This part require half the time of the whole algorithm.…”

Section: Kalman Filtering Algorithm For Speech Enhancement 21 Convenmentioning

confidence: 99%

Noise free Speech Enhancement based on Fast Adaptive Kalman Filtering Algorithm

Banale¹,

Sudhansu²,

Jagde³

2014

IJCA

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Speech enhancement is the process of eliminating noise and increasing the quality of a speech signal, which is contaminated with other kinds of distortions. Conventional Kalman filtering is known as an effective speech enhancement technique, in which speech signal is usually modeled as autoregressive (AR) model and perform a lot of matrix operations. In this paper we proposed a fast adaptive algorithm in presence of environment noise which eliminates the matrix operations and reduces the calculating time by only constantly updating the first value of state vector X(n). To evaluate the system performance we employed the calculation of SNR. Simulation results show that the fast adaptive algorithm using Kalman filtering is effective for speech enhancement. General TermsKalman filter algorithms, Speech enhancement.

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“…In [17], the sampling strong tracking nonlinear UKF was proposed for use in eye tracking. An adaptive UKF [18] based on STF and wavelet transform was presented to further enhance the tracking performance and robustness of standard UKF. The authors in [19] combine particle filter (PF) with the idea of STF and proposed an adaptive PF with strong tracking ability in the case of particle degeneracy and target state mutation.…”

Section: Introductionmentioning

confidence: 99%

“…For the second case, a novel nonlinear filter derived from square-root CKF and the idea of STF was proposed in [23]. However, when the above two cases exist simultaneously, these existing STFs [17][18][19][20][21][22][23] are not suitable for dealing with the filtering problem in the above two coupled cases, and little attention has been paid to the study of deriving the corresponding STF. Consequently, there is a great demand to further improve the STF for the nonlinear discrete-time stochastic dynamic systems with randomly delayed measurements and correlated noises, which motivate this study.…”

Section: Introductionmentioning

confidence: 99%

Strong Tracking Filter for Nonlinear Systems with Randomly Delayed Measurements and Correlated Noises

Yang

Meng

et al. 2018

Mathematical Problems in Engineering

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This paper proposes a novel strong tracking filter (STF), which is suitable for dealing with the filtering problem of nonlinear systems when the following cases occur: that is, the constructed model does not match the actual system, the measurements have the onestep random delay, and the process and measurement noises are correlated at the same epoch. Firstly, a framework of decoupling filter (DF) based on equivalent model transformation is derived. Further, according to the framework of DF, a new extended Kalman filtering (EKF) algorithm via using first-order linearization approximation is developed. Secondly, the computational process of the suboptimal fading factor is derived on the basis of the extended orthogonality principle (EOP). Thirdly, the ultimate form of the proposed STF is obtained by introducing the suboptimal fading factor into the above EKF algorithm. The proposed STF can automatically tune the suboptimal fading factor on the basis of the residuals between available and predicted measurements and further the gain matrices of the proposed STF tune online to improve the filtering performance. Finally, the effectiveness of the proposed STF has been proved through numerical simulation experiments.

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Adaptive unscented Kalman filter for parameter and state estimation of nonlinear high-speed objects

Cited by 38 publications

References 33 publications

A Hybrid Adaptive Unscented Kalman Filter Algorithm

A Hybrid Adaptive Unscented Kalman Filter Algorithm

Noise free Speech Enhancement based on Fast Adaptive Kalman Filtering Algorithm

Strong Tracking Filter for Nonlinear Systems with Randomly Delayed Measurements and Correlated Noises

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