Ellipsoidal constrained state estimation in presence of bounded disturbances

Becis-Aubry, Yasmina

doi:10.23919/ecc54610.2021.9655062

Cited by 7 publications

(6 citation statements)

References 18 publications

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“…It expresses the correlation between the local estimates in an algebraic formulation through the parameterization before deriving the fusion estimates based on the conditions of the local estimates, and the algebraic fusion formulation ensures that the EI fusion algorithm reduces the computational complexity. In contrast, the EI fusion algorithm solves the difficult problem of unknown correlations between local estimates more effectively [28,29].…”

Section: Related Workmentioning

confidence: 99%

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Distributed Ellipsoidal Intersection Fusion Estimation for Multi-Sensor Complex Systems

Peng

Zhou

Liu

et al. 2022

Sensors

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This paper investigates the problem of distributed ellipsoidal intersection (DEI) fusion estimation for linear time-varying multi-sensor complex systems with unknown input disturbances and measurement data transmission delays. For the problem with external unknown input disturbance signals, a non-informative prior distribution is used to model the problem. A set of independent random variables obeying Bernoulli distribution is also used to describe the situation of measurement data transmission delay caused by network channel congestion, and appropriate buffer areas are added at the link nodes to retrieve the delayed transmission data values. For multi-sensor systems with complex situations, a minimum mean square error (MMSE) local estimator is designed in a Bayesian framework based on the maximum a posteriori (MAP) estimation criterion. In order to deal with the unknown correlations among the local estimators and to select the fusion estimator with lower computational complexity, the fusion estimator is designed using ellipsoidal intersection (EI) fusion technique, and the consistency of the estimator is demonstrated. In this paper, the difference between DEI fusion and distributed covariance intersection (DCI) fusion and centralized fusion estimation is analyzed by a numerical example, and the superiority of the DEI fusion method is demonstrated.

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Section: Related Workmentioning

confidence: 99%

“…The positive definite matrix can be the square root decomposed as A = LL T . According to the transformation relation in [28], it is obtained that…”

Section: 𝑥 ∼ 𝒩 𝑥 𝑃mentioning

confidence: 99%

Distributed Ellipsoidal Intersection Fusion Estimation for Multi-Sensor Complex Systems

Peng

Zhou

Liu

et al. 2022

Sensors

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“…In such a way, the thrusts of both actuators j ∈ {l, r} are given by the saturated input signals (8) where φ i (t) and φ i (t) denote the current headings and angular velocities in a body-fixed coordinate frame of each DDboat. The experimentally tuned gains in (8) satisfy the relations K 0 > 0, K P,l = −K P,r and K D,l = −K D,r , respectively.…”

Section: Tracking Control By Means Of An Artificial Potential Field M...mentioning

confidence: 99%

“…As shown in [4], this approach is applicable also in cases in which not all components of the state vector are measured, i.e., if the matrix P m introduced in ( 24) represents a degenerate ellipsoid with bounds that may be infinitely wide in some components of the state space. Although this procedure may be less accurate than the technique presented in [8,29], it is preferred in this paper due to its simplified closed-form solution representation and because it avoids the online solution of linear matrix inequalities.…”

Section: Ellipsoidal Measurement Update Stepmentioning

confidence: 99%

“…In such a way, it becomes possible to verify whether specific maneuvers are guaranteed to be safe, i.e., whether collisions with other vessels or obstacles can be ruled out with certainty. Such guaranteed statements can be made with the help of a set-valued calculus [3][4][5][6][7][8][9]. A suitable representation of uncertainty for a set-valued evaluation of a dynamic system model is the use of ellipsoidal state enclosures.…”

Section: Introductionmentioning

confidence: 99%

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Experimental Validation of Ellipsoidal Techniques for State Estimation in Marine Applications

et al. 2022

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A reliable quantification of the worst-case influence of model uncertainty and external disturbances is crucial for the localization of vessels in marine applications. This is especially true if uncertain GPS-based position measurements are used to update predicted vessel locations that are obtained from the evaluation of a ship’s state equation. To reflect real-life working conditions, these state equations need to account for uncertainty in the system model, such as imperfect actuation and external disturbances due to effects such as wind and currents. As an application scenario, the GPS-based localization of autonomous DDboat robots is considered in this paper. Using experimental data, the efficiency of an ellipsoidal approach, which exploits a bounded-error representation of disturbances and uncertainties, is demonstrated.

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Intelligent state assessment of complex autonomous objects based on wavelet analysis

Kotenko

Saenko

Vinogradenko³

et al. 2023

Engineering Applications of Artificial Intelligence

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Ellipsoidal constrained state estimation in presence of bounded disturbances

Cited by 7 publications

References 18 publications

Distributed Ellipsoidal Intersection Fusion Estimation for Multi-Sensor Complex Systems

Distributed Ellipsoidal Intersection Fusion Estimation for Multi-Sensor Complex Systems

Experimental Validation of Ellipsoidal Techniques for State Estimation in Marine Applications

Intelligent state assessment of complex autonomous objects based on wavelet analysis

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