H/sub ∞/ nonlinear filtering of discrete-time processes

Shaked, U.; Berman, N.

doi:10.1109/78.414787

Cited by 91 publications

(37 citation statements)

References 11 publications

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“…Then consider the infinite-horizon mixed H 2 /H ∞ dynamic game problem with the above filter. Accordingly, we can define the corresponding Hamiltonians H i : T X×W×Y×M n×m → , i = 1, 2, associated with the cost functionals (7), (8) as …”

Section: Definition 51mentioning

confidence: 99%

Mixed ℋ︁₂/ℋ︁_∞ nonlinear filtering

Aliyu

Boukas

2008

Intl J Robust & Nonlinear

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SUMMARYIn this paper, we consider the mixed H 2 /H ∞ filtering problem for affine nonlinear systems. Sufficient conditions for the solvability of this problem with a finite-dimensional filter are given in terms of a pair of coupled Hamilton-Jacobi-Isaacs equations (HJIEs). For linear systems, it is shown that these conditions reduce to a pair of coupled Riccati equations similar to the ones for the control case. Both the finite-horizon and the infinite-horizon problems are discussed. Simulation results are presented to show the usefulness of the scheme, and the results are generalized to include other classes of nonlinear systems.

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Section: Definition 51mentioning

confidence: 99%

Mixed ℋ︁₂/ℋ︁_∞ nonlinear filtering

Aliyu

Boukas

2008

Intl J Robust & Nonlinear

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“…This paper considers the suboptimal solutions that can bound the maximum energy gain from the disturbance to the estimation errors under the prescribed disturbance tolerance level. Given a scalar 2 > 0, find the estimation strategies L such that the ∞ norm of T (L) satisfies [24,25] …”

Section: ∞ Linear Filtermentioning

confidence: 99%

Multitarget Tracking by Improved Particle Filter Based on Unscented Transform

Wang

2013

Mathematical Problems in Engineering

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This paper considers the problem of multitarget tracking in cluttered environment. To reduce the dependency on the noise priori knowledge, an improved particle filtering (PF) data association approach is presented based on the ∞ filter (HF). This approach can achieve higher robustness in the condition that the measurement noise prior is unknown. Because of the limitations of the HF in nonlinear tracking, we first present the ∞ unscented filter (HUF) by embedding the unscented transform (UT) into the ∞ extended filter (HEF) structure. Then the HUF is incorporated into the Rao-Blackwellized particle filter (RBPF) framework to update the particles. Simulation results are provided to demonstrate the effectiveness of the proposed algorithms in linear and nonlinear multitarget tracking.

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“…filter, a robust filter with a modified H performance, can be established for a system with model uncertainty as well as unknown statistical noise properties [3].…”

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confidence: 99%

“…In many cases, it is not easy to obtain the information. Several studies on the nonlinear robust filter have been accomplished [3,8,9,10,15]. The H nonlinear filter with Hamilton-Jacobi inequality (HJI) is one result of such studies, but its computation procedures for obtaining the filter are somewhat complicated [8].…”

mentioning

confidence: 99%

“…To avoid complicated computation procedures, an approximated solution to the robust filtering problem has been recently developed based on a linearization method. The robust filter derived by this approach is called the extended robust filter or extended H filter [3,9]. In [10], the nonlinear state estimation by an approximation is proposed for a nonlinear system with uncertainties described by an integral quadratic constraint (IQC).…”

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confidence: 99%

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An Extended Robust H infinity Filter for Nonlinear Uncertain Systems with Constraints

Seo

Yu²,

Park³

et al.

Proceedings of the 44th IEEE Conference on Decision and Control

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Abstract-In this paper, a robust filter is proposed to effectively estimate the system states in the case where system model uncertainties as well as disturbances are present. The proposed robust filter is constructed based on the linear approximation methods for a general nonlinear uncertain system with an integral quadratic constraint. We also derive the important characteristic of the proposed filter, a modified H performance index. Analysis results show that the proposed filter has robustness against disturbances such as process and measurement noises, and against parameter uncertainties. Simulation results show that the proposed filter effectively improves the performance.

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H/sub ∞/ nonlinear filtering of discrete-time processes

Cited by 91 publications

References 11 publications

Mixed ℋ︁₂/ℋ︁_∞ nonlinear filtering

Mixed ℋ︁₂/ℋ︁_∞ nonlinear filtering

Multitarget Tracking by Improved Particle Filter Based on Unscented Transform

An Extended Robust H infinity Filter for Nonlinear Uncertain Systems with Constraints

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H/sub ∞/ nonlinear filtering of discrete-time processes

Cited by 91 publications

References 11 publications

Mixed ℋ︁2/ℋ︁∞ nonlinear filtering

Mixed ℋ︁2/ℋ︁∞ nonlinear filtering

Multitarget Tracking by Improved Particle Filter Based on Unscented Transform

An Extended Robust H infinity Filter for Nonlinear Uncertain Systems with Constraints

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Mixed ℋ︁₂/ℋ︁_∞ nonlinear filtering

Mixed ℋ︁₂/ℋ︁_∞ nonlinear filtering