A new continuous-discrete particle filter for continuous-discrete nonlinear systems

Xia, Yuanqing; Deng, Zhihong; Li, Li; Geng, Xiumei

doi:10.1016/j.ins.2013.04.030

Cited by 17 publications

(10 citation statements)

References 18 publications

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“…We use two benchmark problems to evaluate the proposed non-Gaussian filters. These problems are called the equilibrium model [6], [17] and the Van der Pol model [12], [18]. The former includes the challenging nonlinearity in the observation model and that makes the posterior state pdfs bimodal.…”

Section: Simulation Studiesmentioning

confidence: 99%

Non-Gaussian Filters for Nonlinear Continuous-Discrete Models

Murata¹,

Hiramatsu²

2017

SICE Journal of Control, Measurement, and System Integration

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In this paper, we propose using an ensemble Kalman filter (EnKF) and particle filters (PFs) to obtain superior state estimation accuracy for nonlinear continuous-discrete models. We discretize the Ito-type stochastic differential system model by means of the usual procedure and suppress the approximation error by using small discrete times. We then simply apply the EnKF and PFs originally developed for nonlinear discrete-time models to the discretized system models, yielding the non-Gaussian filtering algorithms. Since the nonlinear problems generally make the states non-Gaussian as time proceeds, these non-Gaussian filters are promising for improving estimation accuracy. Their filtering performance is evaluated using two benchmark simulation models and compared with the performance of existing Gaussian filters, such as extended and unscented Kalman filters, and with that estimated by using the recursive Cramér-Rao lower bounds.

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Section: Simulation Studiesmentioning

confidence: 99%

Non-Gaussian Filters for Nonlinear Continuous-Discrete Models

Murata¹,

Hiramatsu²

2017

SICE Journal of Control, Measurement, and System Integration

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“…Recently, the PF has been extensively used in the fields of simultaneous localization and mapping (SLAM) for robots [63] and visual tracking [64] . In [65], a continuous-discrete version of particle filter (CD-PF) for continuous-discrete nonlinear systems was proposed and some comparisons between CD-PF and CD-EKF were presented. UKF is also a filtering method used for nonlinear systems.…”

Section: Filtering For Nonlinear Systemsmentioning

confidence: 99%

Recent progress in networked control systems — A survey

Xia

Gao

Yan

et al. 2015

Int. J. Autom. Comput.

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For the past decades, networked control systems (NCSs), as an interdisciplinary subject, have been one of the main research highlights and many fruitful results from different aspects have been achieved. With these growing research trends, it is significant to consolidate the latest knowledge and information to keep up with the research needs. In this paper, the results of different aspects of NCSs, such as quantization, estimation, fault detection and networked predictive control, are summarized. In addition, with the development of cloud technique, cloud control systems are proposed for the further development of NCSs.

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“…As a fundamental research topic in the areas of control and signal processing, the problem of state estimation in nonlinear dynamic systems has received considerable attentions because almost all practical systems involve nonlinearity of one kind or another [1][2][3]. Accordingly, various approaches have been studied, including the extended Kalman filter (EKF) [4,5], Gaussian sum filter [6], Gauss-Hermite filter [7,8], unscented Kalman filter (UKF) [1,9,10] and particle filter [11,12].…”

Section: Introductionmentioning

confidence: 99%

Modified strong tracking unscented Kalman filter for nonlinear state estimation with process model uncertainty

Gao

Zhong

et al. 2015

Adaptive Control & Signal

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This paper presents a modified strong tracking unscented Kalman filter (MSTUKF) to address the performance degradation and divergence of the unscented Kalman filter because of process model uncertainty. The MSTUKF adopts the hypothesis testing method to identify process model uncertainty and further introduces a defined suboptimal fading factor into the prediction covariance to decrease the weight of the prior knowledge on filtering solution. The MSTUKF not only corrects the state estimation in the occurrence of process model uncertainty but also avoids the loss of precision for the state estimation in the absence of process model uncertainty. Further, it does not require the cumbersome evaluation of Jacobian matrix involved in the calculation of the suboptimal fading factor. Experimental results and comparison analysis demonstrate the effectiveness of the proposed MSTUKF.

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A new continuous-discrete particle filter for continuous-discrete nonlinear systems

Cited by 17 publications

References 18 publications

Non-Gaussian Filters for Nonlinear Continuous-Discrete Models

Non-Gaussian Filters for Nonlinear Continuous-Discrete Models

Recent progress in networked control systems — A survey

Modified strong tracking unscented Kalman filter for nonlinear state estimation with process model uncertainty

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