The square-root unscented Kalman filter for state and parameter-estimation

Merwe, Rudolph van der; Wan, Eric A.

doi:10.1109/icassp.2001.940586

Cited by 761 publications

(553 citation statements)

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“…Extended or unscented Kalman filtering are celebrated methods used to identify both the state variables in x and the parameters in γ of a given parametric model structure such as the one provided in (13). Simultaneous identification of state variables and parameters can be done using a "state extension" approach where constant parameters such as those contained in the model parameter vector γ are considered as additional state variables with a rate of change equal to zero.…”

Section: A State Extension and Filteringmentioning

confidence: 99%

“…Simultaneous identification of state variables and parameters can be done using a "state extension" approach where constant parameters such as those contained in the model parameter vector γ are considered as additional state variables with a rate of change equal to zero. In this way, constant parameters are treated as constant functions of time as opposed to constant numbers [13].…”

Section: A State Extension and Filteringmentioning

confidence: 99%

“…Next we turn to model refinement ("Filter" in Algorithm 2) using an Unscented Kalman filter [13]. For this, we consider the following equation forẋ 1 :…”

mentioning

confidence: 99%

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Online model selection for synthetic gene networks

Pan

Menolascina

Stan

2016

2016 IEEE 55th Conference on Decision and Control (CDC)

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Abstract-Control algorithms combined with microfluidic devices and microscopy have enabled in vivo real-time control of protein expression in synthetic gene networks. Most control algorithms rely on the a priori availability of mathematical models of the gene networks to be controlled. These models are typically black/grey box models, which can be obtained through the use of data-driven techniques developed in the context of systems identification. Data-driven inference of both model structure and parameters is the main focus of this paper. There are two main challenges associated with the inference of dynamical models for real-time control of gene regulatory networks in living cells. Since biological systems are typically evolving over time, the first challenge stems from the fact that model selection needs to be done online, which prevents the application of computationally expensive identification algorithms iterating through large amounts of streaming data. The second challenge consists in performing nonlinear model selection, which is typically too burdensome for Kalman filtering related techniques due the heterogeneity and nonlinearity of the candidate models. In this paper, we combine sparse Bayesian techniques with classic Kalman filtering techniques to tackle these challenges.

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Section: A State Extension and Filteringmentioning

confidence: 99%

Section: A State Extension and Filteringmentioning

confidence: 99%

See 1 more Smart Citation

Online model selection for synthetic gene networks

Pan

Menolascina

Stan

2016

2016 IEEE 55th Conference on Decision and Control (CDC)

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“…One approach to reduce the effect of nonlinearities is to apply iteratively the filter (IEKF) as indicated by Zhang [220]. Another solution is to use the Unscented Kalman Filter (UKF), an extension to the EKF that takes into account the nonlinear transformation of means and covariances [105,107,203]. Numerical instability may occur even with the Joseph form of the error covariance matrix.…”

Section: C6 Bibliographical Notesmentioning

confidence: 99%

Environment Learning for Indoor Mobile Robots

Andrade‐Cetto¹

2006

Springer Tracts in Advanced Robotics

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This thesis focuses on the various aspects of autonomous environment learning for indoor service robots. Particularly, on landmark extraction from sensor data, autonomous map building, and robot localization.To univocally identify landmarks from sensor data, we study several landmark representations, and the mathematical foundation necessary to extract the features that build them from images and laser range data. The features extracted from just one sensor may not suffice in the invariant characterization of landmarks and objects, pushing for the combination of information from multiple sources. We present a new algorithm that fuses complementary information from two low level vision modules into coherent object models that can be tracked and learned in a mobile robotics context. Illumination conditions and occlusions are the most prominent artifacts that hinder data association in computer vision. By using photogrammetric and geometric constraints we restrict the search for landmark matches in successive images, and by locking our interest in one or a set of landmarks in the scene, we track those landmarks along successive frames, reducing considerably the data association problem. We concentrate on those tools from the geometry of multiple views that are relevant to the computation of initial landmark location estimates for coarse motion recovery; a desirable characteristic when odometry is not available or is highly unreliable.Once landmarks are accurately extracted and identified, the second part of the problem is to use these observations for the localization of the robot, as well as the refinement of the landmark location estimates. We consider robot motion and sensor observations as stochastic processes, and treat the problem from an estimation theoretic point of view, dealing with noise by using probabilistic methods.The main drawback we encounter is that current estimation techniques have been devised for static environments, and that they lack robustness in more realistic situations. To aid in those situations in which landmark observations might not be consistent in time, we propose a new set of temporal landmark quality functions, and show how iii by incorporating these functions in the data association tests, the overall estimationtheoretic approach to map building and localization is improved. The basic idea consists on using the history of data association mismatches for the computation of the likelihood of future data association, together with the spatial compatibility tests already available.Special attention is paid in that the removal of spurious landmarks from the map does not violate the basic convergence properties of the localization and map building algorithms already described in the literature; namely, asymptotic convergence and full correlation.The thesis also gives an in depth analysis of the fully correlated model to localization and map building from a control systems theory point of view. Considering the fact that the Kalman filter is nothing else but an optimal observer,...

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“…For additional numerical stability and guaranteed semi-definite state covariance matrix, the square-root implementation of the UKF can be used [16]. This type uses the Cholesky decomposition to address certain numerical advantages in the calculation of the transformed statistical properties.…”

Section: Kalman Filtering Proceduresmentioning

confidence: 99%

Identification of a Cessna Citation II Model Based on Flight Test Data

Hoek

Visser

Pool

2017

Advances in Aerospace Guidance, Navigation and Control

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As a result of new aviation legislation, from 2019 on all air-carrier pilots are obliged to go through flight simulator-based stall recovery training. For this reason the Control and Simulation division at Delft University of Technology has set up a task force to develop a new methodology for high-fidelity aircraft stall behavior modeling and simulation. As part of this research project, the development of a new high-fidelity Cessna II simulation model, valid throughout the normal, pre-stall flight envelope, is presented in this paper. From an extensive collection of flight test data, aerodynamic model identification was performed using the Two-Step Method. New in this approach is the use of the Unscented Kalman Filter for an improved accuracy and robustness of the state estimation step. Also, for the first time an explicit data-driven model structure selection is presented for the Citation II by making use of an orthogonal regression scheme. This procedure has indicated that most of the six non-dimensional forces and moments can be parametrized sufficiently by a linear model structure. It was shown that only the translational and lateral aerodynamic force models would benefit from the addition of higher order terms, more specifically the squared angle of attack and angle of sideslip. The newly identified aerodynamic model was implemented into an upgraded version of the existing simulation framework and will serve as a basis for the integration of a stall and post-stall model.

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The square-root unscented Kalman filter for state and parameter-estimation

Cited by 761 publications

References 3 publications

Online model selection for synthetic gene networks

Online model selection for synthetic gene networks

Environment Learning for Indoor Mobile Robots

Identification of a Cessna Citation II Model Based on Flight Test Data

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