Constrained Unscented Gaussian Sum Filter for state estimation of nonlinear dynamical systems

Kottakki, Krishna Kumar; Bhushan, Mani; Bhartiya, Sharad

doi:10.1016/j.compchemeng.2016.04.021

Cited by 13 publications

(4 citation statements)

References 27 publications

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“…Due to its inherent complexity and nonlinearity, this polymerization reactor makes an attractive case study for the current work. Note that the styrene polymerization system has been used to show the performance of state estimation approaches presented in the literature including GSF-based estimation schemes. − The dynamic model for this process is as follows: where …”

Section: Computational Experimentsmentioning

confidence: 99%

Constrained Abridged Gaussian Sum Extended Kalman Filter: Constrained Nonlinear Systems with Non-Gaussian Noises and Uncertainties

Valipour

Ricardez‐Sandoval

2021

Ind. Eng. Chem. Res.

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This work presents a constrained abridged Gaussian sum extended Kalman filter (constrained AGS−EKF) that employs Gaussian mixture models to improve the estimation of extended Kalman filter (EKF) for constrained nonlinear applications involving non-zero mean non-Gaussian process uncertainties and measurement noises. The posterior estimation step in EKF is modified to adopt non-Gaussian measurement noises. An intermediate step is considered to approximate the non-Gaussian prior distribution of the constrained states at each sampling interval. This modified EKF also considers the modified prior estimation step proposed in AGS−EKF (to capture the non-Gaussian process uncertainties). Constrained AGS−EKF performs one (modified) EKF based on the mean value and covariance matrix of the overall Gaussian mixture model, thus avoiding additional computational costs and biased estimations observed in conventional Gaussian sum filters. Computational experiments were performed and showed that the proposed constrained AGS−EKF scheme is computationally efficient and provides appropriate estimates for applications involving active constraints on states, non-Gaussian process uncertainties, and measurement noises.

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Section: Computational Experimentsmentioning

confidence: 99%

Constrained Abridged Gaussian Sum Extended Kalman Filter: Constrained Nonlinear Systems with Non-Gaussian Noises and Uncertainties

Valipour

Ricardez‐Sandoval

2021

Ind. Eng. Chem. Res.

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show abstract

“…The combination strategy is also applied for image restoration and reconstruction [22]. Another idea is to combine the statistical information, such as Bayesian rules [23,24] , Hierarchical clustering [25] and Monte Carlo methods [26]. These approaches attempt to learn the noise model under a certain probability condition, for the purpose of reducing the error caused by the data discretisation.…”

Section: Related Workmentioning

confidence: 99%

Window‐aware guided image filtering via local entropy

Liu

Yang

Wang

2021

IET Image Processing

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Guided image filtering is one of the widely used techniques in computer vision. However, it commonly leads to over‐smoothed edges and a distorted appearance when tackling intricate texture patterns and complex noise. In this paper, a window‐aware image filtering framework based on the bilateral filter guided by the local entropy is presented. The key idea of the authors' proposed approach is to design a novel guidance input and a non‐box filtering window. Specifically, using the Gaussian spatial kernel and the local entropy, a GEF that can maintain image feature details and yield a robust guidance input for BF is constructed. Meanwhile, based on an intensity‐similar strategy, the local non‐box filtering window is designed for the further preservation of edge structures. The authors' approach not only inherits the advantages of bilateral filter i.e. simplicity, parallelisation and easiness of programming, but also is more powerful than bilateral filter and its variants. In addition, the guided entropy filter and the non‐box window can also be transplanted to other local filters and can effectively improve the filtering effects. The qualitative and quantitative experimental results demonstrate that the authors' approach has good performance in image denoising, texture (or background) smoothing, edge extraction and other applications in image processing.

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“…Recently, various filters have been developed to deal with the process uncertain and non-Gaussian system, such as the strong tracking filter [10,11], the model predictive based Kalman filter [12], particle filter (PF) [13,14], Student"s t filter (STF) [15], multiple-model filter (MMF) [16][17][18], and Maximum Correntropy Criterion Kalman filter (MCC-KF) [19][20][21][22][23][24]. The strong tracking filter copes with the process uncertainty problem by employing the time-variant suboptimal fading factor matrix to the state prediction covariance matrix to maintain the residual orthogonal sequence.…”

Section: Introductionmentioning

confidence: 99%

Robust Cubature Kalman Filter for SINS/GPS Integrated Navigation Systems With Unknown Noise Statistics

Feng

Zhang

et al. 2021

IEEE Access

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Constrained Unscented Gaussian Sum Filter for state estimation of nonlinear dynamical systems

Cited by 13 publications

References 27 publications

Constrained Abridged Gaussian Sum Extended Kalman Filter: Constrained Nonlinear Systems with Non-Gaussian Noises and Uncertainties

Constrained Abridged Gaussian Sum Extended Kalman Filter: Constrained Nonlinear Systems with Non-Gaussian Noises and Uncertainties

Window‐aware guided image filtering via local entropy

Robust Cubature Kalman Filter for SINS/GPS Integrated Navigation Systems With Unknown Noise Statistics

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