Gaussian filter for nonlinear systems with correlated noises at the same epoch

Huang, Yulong; Zhang, Yonggang; Wang, Xiaoxu; Zhao, Lin

doi:10.1016/j.automatica.2015.06.035

Cited by 43 publications

(24 citation statements)

References 7 publications

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“…There are actually at least two other intractable uncertainties leading to non-white noises, such as colored noises due to noise correlation in the time direction (Wang et al, 2015) and multiplicative noises due to their dependence on the state (Spinello and Stilwell, 2010;Agamennoni and Nebot, 2014;Wang et al, 2014;Huang et al, 2015;Liu, 2015;Huang et al, 2016a). Noise correlation could occur at the same time instant or one time step apart (or more complicated, multiple time steps apart).…”

Section: Unknown Noisementioning

confidence: 99%

Approximate Gaussian conjugacy: parametric recursive filtering under nonlinearity, multimodality, uncertainty, and constraint, and beyond

Liu

et al. 2017

Frontiers Inf Technol Electronic Eng

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Since the landmark work of R. E. Kalman in the 1960s, considerable efforts have been devoted to time series state space models for a large variety of dynamic estimation problems. In particular, parametric filters that seek analytical estimates based on a closed-form Markov-Bayes recursion, e.g., recursion from a Gaussian or Gaussian mixture (GM) prior to a Gaussian/GM posterior (termed 'Gaussian conjugacy' in this paper), form the backbone for a general time series filter design. Due to challenges arising from nonlinearity, multimodality (including target maneuver), intractable uncertainties (such as unknown inputs and/or non-Gaussian noises) and constraints (including circular quantities), etc., new theories, algorithms, and technologies have been developed continuously to maintain such a conjugacy, or to approximate it as close as possible. They had contributed in large part to the prospective developments of time series parametric filters in the last six decades. In this paper, we review the state of the art in distinctive categories and highlight some insights that may otherwise be easily overlooked. In particular, specific attention is paid to nonlinear systems with an informative observation, multimodal systems including Gaussian mixture posterior and maneuvers, and intractable unknown inputs and constraints, to fill some gaps in existing reviews and surveys. In addition, we provide some new thoughts on alternatives to the first-order Markov transition model and on filter evaluation with regard to computing complexity.

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Section: Unknown Noisementioning

confidence: 99%

Approximate Gaussian conjugacy: parametric recursive filtering under nonlinearity, multimodality, uncertainty, and constraint, and beyond

Liu

et al. 2017

Frontiers Inf Technol Electronic Eng

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“…Beberapa penelitian sebelumnya yang telah menerapkan Filter seperti yang dilakukan oleh Julio et al, (2016), Filter Gaussian yaitu operator konvolusi yang digunakan untuk mengaburkan gambar dan untuk menghilangkan gangguan (noise), Peneliti berikutnya menyimpulkan bahwa keuntungan dari keakuratan estimasi dengan metode filter gaussian dapat diterapkan pada berbagai jenis aplikasi (Huang et al 2015). Penelitian lain menyimpulkan dari hasil uji coba yang telah dilakukan memperlihatkan bahwa Filter Gaussian dapat memberikan perubahan yang lebih sesuai dengan akurasi filter dibandingkan dengan implementasi metode yang lain (Charalampidis 2016).…”

Section: Pendahuluanunclassified

Peningkatan Kualitas Citra CT-Scan dengan Penggabungan Metode Filter Gaussian dan Filter Median

Sumijan

Purnama

Arlis

2019

JTIIK

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AbstrakPerkembangan alat teknologi akuisisi citra medis, satu diantaranya adalah teknologi yang lazim disebut CT-scan. CT-Scan (Computed Tomography Scan) adalah prosedur untuk mendapatkan gambaran dari berbagai area kecil dari tulang termasuk tengkorak kepala dan otak. Citra hasil akuisisi atau rekaman CT-Scan dapat mebantu memperjelas adanya dugaan yang kuat tentang kelainan yang terjadi pada otak. Kualitas citra dapat dilakukan dengan proses mengubah citra menjadi citra baru sesuai kebutuhan, salah satu cara seperti fungsi transformasi, operasi matematis dan pemfilteran. Peningkatan kualitas citra CT-Scan diperlukan untuk objek keputusan medis yang mempunyai kualitas tidak baik, misalnya citra mengalami derau (noise), citra terlalu terang atau gelap, citra kurang tajam, dan kabur. Proses Peningkatan kualitas citra dapat dilakukan dengan menerapkan salah satu metode pemfilteran, untuk memperbaiki kualitas citra agar dihasilkan citra yang lebih baik dari citra aslinya. Metode gaussian filter untuk mengurangi noise speckle dan poisson pada citra otak pada CT-Scan. Pada citra noise gaussian, standar deviasi yang terbaik dalam mengurangi noise bernilai satu. Namun untuk citra noise speckle dan poisson nilai standar tidak dapat mengurangi noise tersebut. Hal ini dikarenakan standar deviasi adalah parameter dalam proses gaussian filter hanya dapat untuk noise Gaussian normal, untuk mengurangi noise sebaran tidak normal (non-linier) digunakan median filter. Kelemahan gaussian filter pada noise nilai parameter tidak stabil (non-linier) dapat diatasi pada filter median. Dari hasil penggabungan filter gaussian dan filter median filter dapat meningkatkan kualitas citra dan menguranggi noise lebih baik sebaran normal dan tidak normal. AbstractThe development of medical image acquisition technology tools, one of which is the technology commonly called CT scan. CT-Scan (Computed Tomography Scan) is a procedure to get a picture of various small areas of bone including the skull and brain. Image acquisition results or CT-Scan recordings can help clarify the existence of strong suspicions about abnormalities that occur in the brain. Image quality can be done by the process of changing the image into a new image as needed, one way such as the transformation function, mathematical operations and filtering. Increasing the quality of CT-Scan images is needed for medical decision objects that have poor quality, for example images experience noise (noise), images are too bright or dark, images are less sharp, and blurred. The process of improving image quality can be done by applying one of the filtering methods, to improve image quality to produce a better image than the original image. Gaussian filter method to reduce speckle and poison noise in brain images on CT scan. In the Gaussian noise image, the best standard deviation in reducing noise is one. However, for speckle noise images and standard poison values it cannot reduce the noise. This is because the standard deviation is a parameter in the Gaussian filter process that can ...

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“…Furthermore, Ito and Xiong investigated the Gaussian filter for nonlinear filtering based on the Gaussian distribution and developed efficient and accurate numerical integration of the optimal filter. The Gaussian filter for nonlinear systems with correlated noises was proposed in the work of Huang et al However, the Gaussian filter with packet dropouts and its performance are not studied to date.…”

Section: Introductionmentioning

confidence: 99%

Modified particle filter and Gaussian filter with packet dropouts

Yang

Fang

2018

Intl J Robust & Nonlinear

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This technical note is concerned with the nonlinear filtering for networked control systems. First, the modified particle filter algorithm with intermittent observations is proposed and the conditional Cramér-Rao lower (CRL) bound with packet dropouts for nonlinear non-Gaussian system is derived. Second, an upper bound for the CRL bound of the Gaussian filter with packet losses is obtained by constructing a linear Gaussian-Markovian networked system because of the complexity in direct analysis and computation. Third, a sufficient condition is given for the bounded expectation of the CRL bound, which is the necessary condition for bounded mean-square error covariance. Finally, an example illustrates the effectiveness of the proposed filter. KEYWORDSCramér-Rao lower bound, networked control systems, packet dropout, particle filter Int J Robust Nonlinear Control. 2018;28:2961-2975.wileyonlinelibrary.com/journal/rnc

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Gaussian filter for nonlinear systems with correlated noises at the same epoch

Cited by 43 publications

References 7 publications

Approximate Gaussian conjugacy: parametric recursive filtering under nonlinearity, multimodality, uncertainty, and constraint, and beyond

Approximate Gaussian conjugacy: parametric recursive filtering under nonlinearity, multimodality, uncertainty, and constraint, and beyond

Peningkatan Kualitas Citra CT-Scan dengan Penggabungan Metode Filter Gaussian dan Filter Median

Modified particle filter and Gaussian filter with packet dropouts

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