Nonlinear Gaussian Filtering With Network-Induced Delay in Measurements

Kumar, Guddu; Nanda, Sumanta Kumar; Verma, Alok Kumar; Bhatia, Vimal; Singh, Abhinoy Kumar

doi:10.1109/taes.2022.3182936

Cited by 14 publications

(11 citation statements)

References 19 publications

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“…Remark 6. Contextualizing the discussion for the proposed filter from Remark 5, it remains exponentially bounded in a mean square if its estimation error satisfies Equation (16). Alternatively, the estimation error satisfies the conditions given in Equation ( 15) (which infers Equation 16) for some a positive definite function V(•).…”

Section: Statementmentioning

confidence: 95%

“…The literature witnesses various developments [11,[14][15][16][17][18][19][20] to handle the delayed measurements independently (considering that no measurement is missing). More specifically, [11] and [15] handle small delays (up to two sampling intervals).…”

Section: Introductionmentioning

confidence: 99%

“…In another contribution, [20] addresses small delays while considering unknown delay probabilities. In further advancements, [19] and [16] utilized similar approaches for handling larger delays. Finally, [17] and [14] handle large delays by stochastically identifying the delay and restructuring the traditional Gaussian filtering method accordingly.…”

Section: Introductionmentioning

confidence: 99%

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Improved Gaussian filtering for handling concurrent delayed and missing measurements

Kumar

Naik

Pachori

et al. 2023

Asian Journal of Control

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This article addresses the Gaussian filtering problem under the environment of jointly occurring delayed and missing measurements. In this work, the former irregularity is incorporated (in the measurement model) using a Bernoulli random variable (BRV) and a geometric random variable, while the latter is subsumed with the help of the BRV; thereby, it enables to take account of large delay extents efficiently. Specifically, a modified measurement model, which incorporates the concerned irregularities, is introduced. Accordingly, the measurement‐related statistical parameters, that is, measurement estimate, covariance, and cross‐covariance, are rederived with respect to the modified measurement model. The rederived parameters replace the corresponding ones in the traditional Gaussian filtering algorithm, resulting in the proposed Gaussian filter. The simulation results conclude the superior performance of the proposed filter over the existing filters in handling the coexisting delay and missing measurement irregularities.

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Section: Statementmentioning

confidence: 95%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Improved Gaussian filtering for handling concurrent delayed and missing measurements

Kumar

Naik

Pachori

et al. 2023

Asian Journal of Control

Self Cite

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“…Based on the different processing domains, image filtering can be classified into spatial and frequency domains. Filtering in the spatial domain is a neighbourhood operation on an image space, the algorithm is simple and fast, but the sharpening effect appears, as in median filtering [1], mean filtering [2], or gaussian filtering [3]. A frequency domain filter converts an image from image space to frequency domain space, which is then analyzed for processing by analyzing its spectrum.…”

Section: Introductionmentioning

confidence: 99%

An overview of digital image analog noise removal based on traditional filtering

Zhang

2023

International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023)

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Due to interference in the transmission of external equipment, images can suffer from varying noise concentrations. As an immediate and practical step to reduce the impact of noise on an image and to improve its quality and visual presentation, filtering is the best method. The paper examines how traditional filtering methods restore a digital image under salt and pepper and Gaussian noise interference. Also included in this paper is an analysis of the advantages and challenges of traditional noise filtering, as well as suggestions for a filtering method for a variety of noise levels. Lastly, the effects of filtered noise pictures are evaluated through extensive experimental simulations, providing substantial assistance for related research.

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“…This adapts well to nonlinear and non-Gaussian systems. Other recent development include heuris-tics for filtering with irregular measurements [14], [15] and assumed Gaussian density filtering with non-Gaussian noise [16].…”

Section: Introductionmentioning

confidence: 99%

Wrapped Particle Filtering for Angular Data

et al. 2022

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Particle filtering is probably the most widely accepted methodology for general nonlinear filtering applications. The performance of a particle filter critically depends on the choice of proposal distribution. In this paper, we propose using a wrapped normal distribution as a proposal distribution for angular data, i.e. data within finite range (−π, π]. We then use the same method to derive the proposal density for a particle filter, in place of a standard assumed Gaussian density filter such as the unscented Kalman filter. The numerical integrals with respect to wrapped normal distribution are evaluated using Rogers-Szegő quadrature. Compared to using the unscented filter and similar approximate Gaussian filters to produce proposal densities, we show through examples that wrapped normal distribution gives a far better filtering performance when working with angular data. In addition, we demonstrate the trade-off involved in particle filters with local sampling and global sampling (i.e. by running a bank of approximate Gaussian filters vs running a single approximate Gaussian filter) with the former yielding a better filtering performance than the latter at the cost of increased computational load. INDEX TERMSNonlinear dynamical systems, Angular data, Particle filtering, Wrapped normal distribution, and Rogers-Szegő quadrature rule.

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Nonlinear Gaussian Filtering With Network-Induced Delay in Measurements

Cited by 14 publications

References 19 publications

Improved Gaussian filtering for handling concurrent delayed and missing measurements

Improved Gaussian filtering for handling concurrent delayed and missing measurements

An overview of digital image analog noise removal based on traditional filtering

Wrapped Particle Filtering for Angular Data

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