H filtering and solution bound for non-linear systems

Yung, Chee‐Fai; Li, Yen-Fang; Sheu, Hsin-Teng

doi:10.1080/00207170010018797

Cited by 31 publications

(13 citation statements)

References 5 publications

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“…Currently, many robust tracking researches are under H co performance which guarantees the gain from the external disturbance to the estimation error to be less than a prescribed level [12]. One of the main advantages of robust H co fIltering is that it is not necessary to know exactly the statistical properties of the external disturbance but only assumes that the external disturbances have a bounded energy.…”

Section: School Of Electronics Information Engineeringmentioning

confidence: 99%

Research on robust GNSS vehicle three-dimensional tracking method for urban elevated road networks

Ma¹,

Xing²,

Gao³

et al. 2011

2011 IEEE 3rd International Conference on Communication Software and Networks

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Ahstract-The Kalman filtering(KF) has been implemented as the primary scheme for many land vehicle navigation and positioning applications. However, it has been reported that the KF-based techniques have limitations that it assumes the noise is Gaussian white and the system model must be known exactly. Due to the complicated vehicle tracking environment in urban area(signal disappear, attenuation or reflection) and diverse vehicle motion(uniform or accelerated), The VNS inevitably exits stochastic uncertainties whose statistical property can not be priori known. This makes great difficulties in tracking vehicle robustly. In this paper, robust GNSS vehicle three-dimensional tracking method for urban elevated road networks is investigated. By exploring the geometry of the vehicle tracking problem, the three-dimensional vehicle tracking problem is formulated to one-dimensional target trajectory tracking problem. Accounting for modeling uncertainties and unpredictable disturbances problem, via robust Hoc filtering algorithm with Stochastic Uncertainties that we have developed in another paper, a three-dimensional vehicle tracking algorithm for urban elevated road networks is proposed. The experiment results confirm the effectiveness of the proposed method by comparing with the Kalman filter tracking method using the measured GNSS data.

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Section: School Of Electronics Information Engineeringmentioning

confidence: 99%

Research on robust GNSS vehicle three-dimensional tracking method for urban elevated road networks

Ma¹,

Xing²,

Gao³

et al. 2011

2011 IEEE 3rd International Conference on Communication Software and Networks

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“…In recent years, with the development of the H ∞ control theory, considerable attention has been devoted to H ∞ filtering since it, in contrast to the celebrated Kalman filtering, has an attractive property that the disturbance signal is required only to be of bounded energy rather than Gaussian (Anderson & Moore, 1979). The so-called H ∞ filtering is to design an estimator to estimate the unknown state combination via output measurement, which guarantees the energy gain from the external disturbance to the estimation error no greater than a prescribed level (Berman & Shaked, 1996;Shen & Deng, 1999;Yung, Li, & Sheu, 2001). Since stochastic modelling plays a vital role in engineering applications, the H ∞ filtering theory has naturally been extended to stochastic systems from deterministic systems.…”

Section: Introductionmentioning

confidence: 98%

Finite-timeH_∞filtering for non-linear stochastic systems

Hou

Deng

Duan

2015

International Journal of Systems Science

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This paper describes the robust H ∞ filtering analysis and the synthesis of general non-linear stochastic systems with finite settling time. We assume that the system dynamic is modelled by Itô-type stochastic differential equations of which the state and the measurement are corrupted by state-dependent noises and exogenous disturbances. A sufficient condition for non-linear stochastic systems to have the finite-time H ∞ performance with gain less than or equal to a prescribed positive number is established in terms of a certain Hamilton-Jacobi inequality. Based on this result, the existence of a finite-time H ∞ filter is given for the general non-linear stochastic system by a second-order non-linear partial differential inequality, and the filter can be obtained by solving this inequality. The effectiveness of the obtained result is illustrated by a numerical example.

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“…The authors also derive matrix inequalities helpful in solving this type of problems. Since then, various methods have been reported in the literature to design robust observers for nonlinear systems [3][4][5][6][7][8][9][10][11][12]. In [13,14], an observer design method is proposed using a dynamic observer structure.…”

Section: Introductionmentioning

confidence: 99%