Introduction to Coupled Data Technologies

Ho, Ron; Drost, Robert

doi:10.1007/978-1-4419-6588-2_1

Cited by 1 publication

(1 citation statement)

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“…Remark 3. In practical systems, extrinsic noises are typically modeled as unbounded Gaussian stochastic processes, even though natural intrinsic perturbations, such as crosstalk and power supply noise, 52 quantization noise distribution, 14 jump within systems and so on, are bounded. The nature of real physical quantity depends on the system structure that is always bounded.…”

Section: Non-linear Non-gaussian Systemmentioning

confidence: 99%

Quadratic covariance‐constrained filtering for linear and non‐linear systems with non‐Gaussian noises

Javanfar

Rahmani

2023

Optim Control Appl Methods

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This study considers a robust quadratic covariance‐constrained filtering problem for discrete time‐varying linear and non‐linear dynamic systems with non‐Gaussian noises. Non‐Gaussian noises are presumed to be unknown, bounded, and limited in a specified ellipsoidal set. In this approach, first, a general standard linear form of the filter is introduced for state estimation in linear dynamic systems. The filter gain is obtained by minimizing the upper bound of the estimation error's covariance matrix. The Lyapunov theory demonstrates the stability of this filter. Second, we extend the proposed filtering approach to non‐linear dynamic systems that are considered as a combination of linear and non‐linear terms. The Lipschitz‐like condition is assumed for the non‐linear part. A new filter structure is proposed in this case and the filter gain is obtained by the same idea to minimize the upper bound of the error's covariance matrix. Finally, four numerical examples are presented to signify the effectiveness and performance of the proposed filters for linear and non‐linear systems.

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Section: Non-linear Non-gaussian Systemmentioning

confidence: 99%