Stochastic Integration Filter

Duník, Jindřich; Straka, Ondřej; Šimandl, Miroslav

doi:10.1109/tac.2013.2258494

Cited by 96 publications

(54 citation statements)

References 18 publications

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“…The TUKF purportedly can address the nonlocal sampling problem inherent in the CKF while maintaining the virtue of numerical stability for high dimensional problems. Dunik et al [89] pointed out that traditional filters providing local estimates of the state, such as the EKF, UKF, or CKF, are based on computationally efficient but approximate integral evaluations, and therefore proposed a general local filter that utilizes stochastic integration methods providing the asymptotically exact integral evaluation with computational complexity similar to the traditional filters.…”

Section: Ckfmentioning

confidence: 99%

State-of-the-Art Mobile Intelligence: Enabling Robots to Move Like Humans by Estimating Mobility with Artificial Intelligence

Kong

Bai

et al. 2018

Applied Sciences

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Mobility is a significant robotic task. It is the most important function when robotics is applied to domains such as autonomous cars, home service robots, and autonomous underwater vehicles. Despite extensive research on this topic, robots still suffer from difficulties when moving in complex environments, especially in practical applications. Therefore, the ability to have enough intelligence while moving is a key issue for the success of robots. Researchers have proposed a variety of methods and algorithms, including navigation and tracking. To help readers swiftly understand the recent advances in methodology and algorithms for robot movement, we present this survey, which provides a detailed review of the existing methods of navigation and tracking. In particular, this survey features a relation-based architecture that enables readers to easily grasp the key points of mobile intelligence. We first outline the key problems in robot systems and point out the relationship among robotics, navigation, and tracking. We then illustrate navigation using different sensors and the fusion methods and detail the state estimation and tracking models for target maneuvering. Finally, we address several issues of deep learning as well as the mobile intelligence of robots as suggested future research topics. The contributions of this survey are threefold. First, we review the literature of navigation according to the applied sensors and fusion method. Second, we detail the models for target maneuvering and the existing tracking based on estimation, such as the Kalman filter and its series developed form, according to their model-construction mechanisms: linear, nonlinear, and non-Gaussian white noise. Third, we illustrate the artificial intelligence approach-especially deep learning methods-and discuss its combination with the estimation method.

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Section: Ckfmentioning

confidence: 99%

State-of-the-Art Mobile Intelligence: Enabling Robots to Move Like Humans by Estimating Mobility with Artificial Intelligence

Kong

Bai

et al. 2018

Applied Sciences

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“…The moments can be understood as a Gaussian approximation of the conditional PDF, i.e., p(x k |z k ) ≈ N {x k :x k|k , P xx k|k } [5], depending on the type of used approximation.…”

Section: State Estimation By Local Filtersmentioning

confidence: 99%

“…For the approximations the local filters (LFs) may utilize function approximation using polynomial expansions, e.g., the Taylor or Stirling expansions [11]- [15] leading to the extended Kalman filter (EKF), the second order EKF, or the divided difference filters. Or they may use other means (such as sigma points) of approximation leading to e.g., the unscented Kalman filter (UKF) [16], [17], Gauss-Hermite filter [15], quadrature filters [18], the cubature Kalman filter (CKF) [4], [19] or the stochastic integration filter (SIF) [5].…”

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confidence: 99%

Structure adaptation of nonlinear filters based on non-Gaussianity measures

Straka

Duník

Šimandl

2015

2015 American Control Conference (ACC)

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The paper deals with state estimation of stochastic nonlinear dynamical systems. A structure adaptation of nonlinear filters is proposed to reduce errors stemming from approximations made by the filters. The adaptation is controlled by non-Gaussian measures which assess current working conditions of the filter. A large non-Gaussian measure indicates a possible large approximation error and results in splitting the state conditional probability density function. To limit computational complexity of the filter given by the number of terms, a reduction of this number is done by merging some terms. The algorithm of the proposed filter with structure adaptation is detailed using the extended Kalman filter relations. Performance of the filter is illustrated in a numerical example.

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“…The integrands are the products of nonlinear functions and Gaussian pdfs [1]. The local approaches approximate either the nonlinear functions or the Gaussian pdfs [3]. Typical filters in the former group are the extended Kalman filter (EKF) [4] and divided difference filters (DDFs) [5].…”

Section: Introductionmentioning

confidence: 99%

Spherical Simplex-Radial Cubature Kalman Filter

Wang

Feng

Tse

2014

IEEE Signal Process. Lett.

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In this letter, a new class of cubature Kalman filters (CKF) based on a spherical simplex-radial rule is proposed to further improve accuracy and efficiency of the traditional CKF. The transformation group of the regular simplex and the moment matching method are utilized to compute the spherical and radial integrals, respectively. In addition, with the increase of the state dimension, the new CKF of the fifth degree uses fewer quadrature points than the traditional CKF with the same degree. Simulations on moments calculation and nonlinear state estimation show that the proposed CKFs can achieve higher accuracy and better efficiency than the traditional CKFs.Index Terms-Bayesian filter, cubature kalman filter, numerical integration, simplex, spherical-radial.

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Stochastic Integration Filter

Cited by 96 publications

References 18 publications

State-of-the-Art Mobile Intelligence: Enabling Robots to Move Like Humans by Estimating Mobility with Artificial Intelligence

State-of-the-Art Mobile Intelligence: Enabling Robots to Move Like Humans by Estimating Mobility with Artificial Intelligence

Structure adaptation of nonlinear filters based on non-Gaussianity measures

Spherical Simplex-Radial Cubature Kalman Filter

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