Results 261 to 270 of about 39,931 (297)

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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Comparison between Kalman Filter and Adaptive Filter

International Journal for Scientific Research
This study contains a comparison between the two Kalman filter and adaptive filter has huge importance because of it is uses in different applications such as air navigation planes, satellites, cameras, motions, radar, stations. Adaptive filter has various Application as well and important in CCTV and microphones.
Mohamed Mohamed, Noha Saleh
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Adaptive Two Stage Kalman Filter

2015
Kalman Filtresi yönteminde sistem dinamiği, parametreleri ve istatistiksel özelliklerinin tam olarak bilindiği varsayımı yapılır. Fakat birçok gerçek uygulamada sistem modeli bilinmeyen rasgele veya sabit sapmalar içerir ve bu nedenle Kalman Filtresinde ıraksamalar meydana gelebilir. Bu bilinmeyen sabit veya rasgele sapmaların modele dahil edilmesi ile
BABACAN, Esin Köksal, BİÇER, Cenker
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Adaptive Kalman Filtering

2020
Chingiz Hajiyev, Halil Ersin Soken
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