Nonlinear distributed state estimation on the Stiefel manifold using diffusion particle filters

Figueredo, Caio G. de; Bordin, Claudio J.; Bruno, Marcelo G. S.

doi:10.1016/j.dsp.2021.103354

Cited by 6 publications

(2 citation statements)

References 32 publications

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“…To evaluate the performance of the proposed algorithms we ran Monte Carlo simulations consisting of 1,000 independent runs. We used a network with five nodes: nodes 1 to 4 are on the vertices of a square and node 5 is at its center and is connected to all other nodes, as in [19], [20]. The noise covariance matrices were set to Ω r = I and Γ r = I•10 −αr/10 , with α r equal to 3, 6, 10, 13 and 20 dB for r = 1, .…”

Section: Simulation Resultsmentioning

confidence: 99%

“…In previous works [19], [20], we introduced diffusion particle filter (PF) algorithms for fully distributed estimation of hidden variables that were constrained to the unit hypersphere and the Stiefel manifold, respectively. The algorithms in [19], [20] employed both the Random Exchange (RndEx) [21] and the Adapt-then-Combine (ATC) [16], [22] diffusion techniques and used methods from directional statistics to represent probability density functions (p.d.f. 's) that are transmitted between nodes over the network.…”

Section: Introductionmentioning

confidence: 99%

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Diffusion Particle Filtering on the Special Orthogonal Group Using Lie Algebra Statistics

Bordin

Figueredo²,

Bruno

2022

IEEE Signal Process. Lett.

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In this paper, we introduce new distributed diffusion algorithms to track a sequence of hidden random matrices that evolve on the special orthogonal group. The algorithms are based on the Adapt-then-Combine and the Random Exchange methods, and diffuse Gaussian approximations of posterior densities computed in the Lie algebra of the special orthogonal group. Simulation results show that, in a scenario with strongly nonlinear observation functions, the proposed algorithms perform similarly to the centralized particle filter estimator and can outperform competing Extended Kalman Filters.

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Section: Simulation Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Diffusion Particle Filtering on the Special Orthogonal Group Using Lie Algebra Statistics

Bordin

Figueredo²,

Bruno

2022

IEEE Signal Process. Lett.

Self Cite

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Adaptive parameter estimation for the expanded sandwich model

Yang

Zhang

Liu

et al. 2023

Sci Rep

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An expanded-sandwich system is a nonlinear extended block-oriented system in which memoryless elements in conventional block-oriented systems are displaced by memory submodels. Expanded-sandwich system identification has received extensive attention in recent years due to the powerful ability of these systems to describe actual industrial systems. This study proposes a novel recursive identification algorithm for an expanded-sandwich system, in which an estimator is developed on the basis of parameter identification error data rather than the traditional prediction error output information. In this scheme, a filter is introduced to extract the available system information based on miserly structure layout, and some intermediate variables are designed using filtered vectors. According to the developed intermediate variables, the parameter identification error data can be obtained. Thereafter, an adaptive estimator is established by integrating the identification error data compared with the classic adaptive estimator based on the prediction error output information. Thus, the design framework introduced in this research provides a new perspective for the design of identification algorithms. Under a general continuous excitation condition, the parameter estimation values can converge to the true values. Finally, experimental results and illustrative examples indicate the availability and usefulness of the proposed method.

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Distributed Particle Filters for State Tracking on the Stiefel Manifold Using Tangent Space Statistics

Bordin

Figueredo

Bruno

2022

ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Nonlinear distributed state estimation on the Stiefel manifold using diffusion particle filters

Cited by 6 publications

References 32 publications

Diffusion Particle Filtering on the Special Orthogonal Group Using Lie Algebra Statistics

Diffusion Particle Filtering on the Special Orthogonal Group Using Lie Algebra Statistics

Adaptive parameter estimation for the expanded sandwich model

Distributed Particle Filters for State Tracking on the Stiefel Manifold Using Tangent Space Statistics

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