Results 151 to 160 of about 35,356 (190)

An Adaptive Ensemble Kalman Filter

Monthly Weather Review, 2000
Abstract To the extent that model error is nonnegligible in numerical models of the atmosphere, it must be accounted for in 4D atmospheric data assimilation systems. In this study, a method of estimating and accounting for model error in the context of an ensemble Kalman filter technique is developed.
Herschel L. Mitchell, P. L. Houtekamer
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Robust Adaptive Kalman Filtering with Unknown Inputs

1986 American Control Conference, 1986
The conventional sequential adaptive procedure for estimating noise covariances and input forcing function has suboptimal performance and potential instability. In this work we present a robust procedure for optimally estimating a polynomial-form input forcing function, its time of occurrence and the measurement error covariance matrix, R.
A. Moghaddamjoo, R.L. Kirlin
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Input-adaptive Kalman-Bucy filtering

IEEE Transactions on Automatic Control, 1970
The theory of nonlinear filtering (Stratonovitch, Kushner, and Bucy) is applied 1) to real-time identification of the covariance matrix of the input noise in the process model used by Kalman and Bucy [1] and 2) to adaptive mechanization of the matrix Riccati equation and the gain matrix in the Kalman-Bucy filter.
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A Neurofuzzy Adaptive Kalman Filter

2006 3rd International IEEE Conference Intelligent Systems, 2006
In this work the recently developed fuzzy logic-based adaptive Kalman filter (FL-AKF) is integrated into a neurofuzzy network structure to perform system identification and state estimation of unknown nonlinear systems. This approach, referred to as neurofuzzy adaptive Kalman filter, uses the error signal in the identification process as the ...
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Kalman Filter Modification in Adaptive Control

Journal of Guidance, Control, and Dynamics, 2010
This paper presents a novel Kalman-filter-based approach for approximately enforcing a linear constraint in adaptive control. One application is that this leads to alternative forms for well-known modification terms such as e modification. It is shown that employing this approach does not increase the theoretical guaranteed ultimate bounds for the ...
Tansel Yucelen, Anthony J. Calise
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Adaptive Kalman filtering for target tracking

2016 IEEE/OES China Ocean Acoustics (COA), 2016
Kalman filtering is widely used in target tracking. However, conventional Kalman filtering may fail to track the target when there is acceleration, deceleration, and turn. In this paper, these maneuvers are characterized by two orthogonal components of the changed velocity in l 1 -norm.
Feng Xiao, Mingyu Song, Xin Guo, Fengxiang Ge   +3 more
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Tracking targets using adaptive Kalman filtering

IEEE Transactions on Aerospace and Electronic Systems, 1990
A simple algorithm for estimating the unknown process noise variance of an otherwise known linear plant, using a Kalman filter is suggested. The process noise variance estimator is essentially dead beat, using the difference between the expected prediction error variance, computed in the Kalman filter, and the measured prediction error variance.
P.-O. Gutman, M. Velger
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Adaptive Kalman Filtering by Covariance Sampling

IEEE Signal Processing Letters, 2017
It is well known that the performance of the Kalman filter deteriorates when the system noise statistics are not available a priori . In particular, the adjustment of measurement noise covariance is deemed paramount as it directly affects the estimation accuracy and plays the key role in applications such as sensor selection and sensor fusion.
Akbar Assa, Konstantinos N. Plataniotis
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A novel adaptive unscented Kalman filter

2012 Third International Conference on Intelligent Control and Information Processing, 2012
For solving the problem that the conventional unscented Kalman filter (UKF) declines in accuracy and further diverges when the system's noise statistics are unknown and time-varying, an adaptive UKF is proposed based on moving window and random weighting methods.
Gaoge Hu, Shesheng Gao, Li Xue
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Adaptive Unscented Kalman Filter

2011
Bilinen Geleneksel Kalman Filtresi doğru bir sistem modeli ve tam stokastik bilgi gerektirir. Fakat, pek çok gerçek uygulamada sistem bilgisi tam olarak bilinmez veya yanlış bilinir. Bu nedenle filtre ıraksayabilir veya yanlı tahminler elde edilebilir.
KÖKSAL BABACAN, Esin, ÖZBEK, Levent, BİÇER, Cenker   +2 more
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