Exponentially Fitted Cubature Kalman Filter With Application to Oscillatory Dynamical Systems

Singh, Abhinoy Kumar

doi:10.1109/tcsi.2020.2985867

Cited by 9 publications

(2 citation statements)

References 38 publications

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“…However, the computation of mean and covariance involves integrals which are unavailable in close form and need to be numerically approximated during filtering. Some of the popular Gaussian filters are the extended Kalman filter (EKF) [1], the unscented Kalman filter (UKF) [12], the cubature Kalman filter (CKF) [13], the cubature quadrature Kalman filter (CQKF) [14], the Gauss-Hermite filter (GHF) [15], and the exponentially-fitted CKF (ECKF) [16]. The Gaussian filters are sub-optimal, mainly due to the Gaussian approximation of arbitrary prior and posterior PDFs.…”

Section: Introductionmentioning

confidence: 99%

Gaussian Filtering for Simultaneously Occurring Delayed and Missing Measurements

et al. 2022

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Approximate filtering algorithms in nonlinear systems assume Gaussian prior and predictive density and remain popular due to ease of implementation as well as acceptable performance. However, these algorithms are restricted by two major assumptions: they assume no missing or delayed measurements. However, practical measurements are frequently delayed and intermittently missing. In this paper, we introduce a new extension of the Gaussian filtering to handle the simultaneous occurrence of the delay in measurements and intermittently missing measurements. Our proposed algorithm uses a novel modified measurement model to incorporate the possibility of the delayed and intermittently missing measurements. Subsequently, it redesigns the traditional Gaussian filtering for the modified measurement model. Our algorithm is a generalized extension of the Gaussian filtering, which applies to any of the traditional Gaussian filters, such as the extended Kalman filter (EKF), unscented Kalman filter (UKF), and cubature Kalman filter (CKF). A further contribution of this paper is that we study the stochastic stability of the proposed method for its EKF-based formulation. We compared the performance of the proposed filtering method with the traditional Gaussian filtering (particularly the CKF) and three extensions of the traditional Gaussian filtering that are designed to handle the delayed and missing measurements individually or simultaneously.INDEX TERMS Delayed measurements, Gaussian filtering, missing measurements, nonlinear Bayesian filtering.

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Section: Introductionmentioning

confidence: 99%

Gaussian Filtering for Simultaneously Occurring Delayed and Missing Measurements

et al. 2022

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“…But, the estimation accuracy of the UKF is limited for higher-order systems analysis. The CKF [3] can be developed and being widely applied into various real world estimation problems in [6], [7], [8], [9], [10], [11], [12].…”

Section: Introductionmentioning

confidence: 99%

A new state estimation approach-Adaptive Fading Cubature Kalman filter

Narasimhappa

2021

Preprint

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This paper presents a novel adaptive fading cubature Kalman filter (AFCKF) based on double transitive factors. The developed adaptive algorithm is explained in two stages; stage (i) a single transitive factor is used to update the predicted state error covariance, Pk − based on innovation or residual vector, whereas, in stage (ii), the measurement noise covariance matrix, R * k is scaled by another transitive factor. Furthermore, showing the proof concept for estimation of the process noise, Q * k and measurement noise covariance matrices by combining the innovation and residual vector in the AFCKF algorithm. It can provide reliable state estimation in the presence of unknown noise statistics. Bench-marking target tracking example is consider to show the performance improvement of the developed algorithms. As compared with existing adaptive approaches, the proposed fading algorithm can provide better estimation results.

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Reliability-based Event Driven Backstepping Positioning Control for a Turret-moored FPSO Vessel with Unknown Slow Time-varying Disturbances

Tuo

Wang

Guo

et al. 2022

Int. J. Control Autom. Syst.

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Exponentially Fitted Cubature Kalman Filter With Application to Oscillatory Dynamical Systems

Cited by 9 publications

References 38 publications

Gaussian Filtering for Simultaneously Occurring Delayed and Missing Measurements

Gaussian Filtering for Simultaneously Occurring Delayed and Missing Measurements

A new state estimation approach-Adaptive Fading Cubature Kalman filter

Reliability-based Event Driven Backstepping Positioning Control for a Turret-moored FPSO Vessel with Unknown Slow Time-varying Disturbances

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