Truncation nonlinear filters for state estimation with nonlinear inequality constraints

Straka, Ondřej; Duník, Jindřich; Šimandl, Miroslav

doi:10.1016/j.automatica.2011.11.002

Cited by 57 publications

(39 citation statements)

References 37 publications

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“…4, the proposed nonaugmented GF recursively operates by combining the analytical computation in (26) with the nonlinear Gaussian integrals in (18)- (24) and (27)- (29). The heart of implementing such GF is thus transformed to compute these nonlinear integrals.…”

Section: Theorem 1 Consider the System In (1)-(2) With The Given Initmentioning

confidence: 99%

“…Further, GSF provides a feasible approach to meet both accuracy and real time required in the filtering problem of nonlinear time-delay systems. However, the existing literatures concerning GSF or GF have been limited to the research on the robust filter for dealing with the model error [13], on the adaptive filter with Gaussian sum refinement and coarsening [12,14], on the numerical technologies for approximating the nonlinear integrals [15][16][17][18][19][20], on the state smoothing framework design [10,[21][22][23], on the numerical and stability analyses [24][25][26][27][28], on the constrained estimation [29][30][31], and on the colored and correlated noises [9,10]. So far, there are seldom results paying adequate research attention on designing the GSF for the nonlinear time-delay systems despite GSF has been significantly shown to be good at both accuracy and real time.…”

Section: Each Constituent Gf Essentially Acts Independentlymentioning

confidence: 99%

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Gaussian sum approximation filter for nonlinear dynamic time-delay system

Wang

Liang

2015

Nonlinear Dyn

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This paper focuses on designing Gaussian sum approximation filter for both accurately and rapidly estimating the state of a class of nonlinear dynamic time-delay systems. Firstly, a novel nonaugmented Gaussian filter (GF) is derived, whose superiority in computation efficiency is theoretically analyzed as compared to the standard augmented GF. Secondly, a nonaugmented Gaussian sum filter (GSF) is proposed to accurately capture the state estimates by a weight sum of the above-proposed GF. In GSF, each GF component is independent from the others and can be performed in a parallel manner so that GSF is conducive to high-performance computing across many compute nodes. Finally, the performance of the proposed GSF is demonstrated by a vehicle suspension system with time delay, where the GSF achieves higher accuracy than the single GF and is computationally much more efficient than the particle filter with the almost same accuracy.

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Section: Theorem 1 Consider the System In (1)-(2) With The Given Initmentioning

confidence: 99%

Section: Each Constituent Gf Essentially Acts Independentlymentioning

confidence: 99%

Gaussian sum approximation filter for nonlinear dynamic time-delay system

Wang

Liang

2015

Nonlinear Dyn

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show abstract

“…Different algorithms can be applied for the updating, in our work, the truncated unscented Kalman filtering (t-UKF) scheme as in [13] is adopted, which exploits both the constraint information and unscented transformation to better deal with the non-linearities in both dynamic and measurement models.…”

Section: (11)mentioning

confidence: 99%

New Environmental Dependent Modelling with Gaussian Particle Filtering Based Implementation for Ground Vehicle Tracking

Yu¹,

Xue²,

Ding³

et al. 2016

2016 Sensor Signal Processing for Defence (SSPD)

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“…For example, it includes signal filtering, smoothing and prediction problems, estimation of signal deconvolution, white noise deconvolution problem etc. Signal estimation can be regarded as a special form of state estimation [1][2][3]. For example, estimation of ARMA signals can be transformed into a state estimation problem, while the signal is the component of the state.…”

Section: Introductionmentioning

confidence: 99%