Results 231 to 240 of about 67,963 (266)

A globally convergent adaptive IIR filter

2000 IEEE International Symposium on Circuits and Systems. Emerging Technologies for the 21st Century. Proceedings (IEEE Cat No.00CH36353), 2002
An open issue in Adaptive IIR Filtering (AIF) is that of convergence to a global minimum in the presence of observation noise, when the system is insufficiently modeled, or when the excitation source is colored. It is well known that algorithms based on Equation Error (EE) contain a single minimum that may be biased whereas, algorithms based on Output ...
Afshin David, Tyseer Aboulnasr
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Asymptotic convergence of the ensemble Kalman filter

2008 15th IEEE International Conference on Image Processing, 2008
This paper formally addresses the asymptotic convergence of the ensemble Kalman filter (EnKF), a state estimation procedure that, when combined with a technique called localization, provides computationally tractable solutions to large-dimensional state estimation problems.
Mark D. Butala, Jonghyun Yun, Yuguo Chen, Richard A. Frazin, Farzad Kamalabadi   +4 more
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Convergence analysis of cubature Kalman filter

2014 European Control Conference (ECC), 2014
This paper investigates the stability analysis of cubature Kalman filter (CKF) for nonlinear systems with linear measurement. The certain conditions to ensure that the estimation error of CKF remains bounded are proved. Then, the effect of process noise covariance is investigated and an adaptive process noise covariance is proposed to deal with large ...
Zarei, Jafar, Shokri, Ehsan, KARIMI, HAMID REZA   +2 more
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Semi-convergence of filters and nets

Mathematical Journal of Okayama University, 1999
Summary: In [Am. Math. Mon. 70, 36-41 (1963; Zbl 0113.16304)] \textit{N. Levine} introduced the concept of semi-open set and semi-continuity. Semi-convergence and semi-compactness were first introduced, investigated and characterized by \textit{C. Dorsett} in [Ann. Soc. Sci. Brux., Ser. I 92, 143-150 (1978; Zbl 0408.54009)] and [Indian J. Mech.
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Convergence properties of a decentralized Kalman filter

2008 47th IEEE Conference on Decision and Control, 2008
We consider the problem of decentralized Kalman filtering in a sensor network. Each sensor node implements a local Kalman filter based on its own measurements and the information exchanged with its neighbors. It combines the information received from other sensors through using a consensus filter as proposed in [14].
Kamgarpour, Maryam, Tomlin, Claire
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Convergence results for the particle PHD filter

IEEE Transactions on Signal Processing, 2006
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Daniel Edward Clark, Judith Bell
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Normalization and convergence of adaptive IIR filters

IEEE International Conference on Acoustics Speech and Signal Processing, 1993
Several algorithms for adaptive IIR (infinite impulse response) filters have been proposed. However, their practical use involves considerations such as finding the global minimum, the possible occurrence of instability, and uncertainty about the speed of convergence. A generalization of these adaptive IIR algorithms is presented here.
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Convergence of Kalman filter with quantized innovations

2010 11th International Conference on Control Automation Robotics & Vision, 2010
This work provides a convergence analysis for the estimate error covariance of Kaiman filtering based on quantized measurement innovations (QIKF). By taking the quantization errors as random perturbations in observation system, an equivalent state-observation system is given.
Jian Xu, Jianxun Li, Jiayun Wu
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Convergence Properties of the Kalman Inverse Filter

Journal of Dynamic Systems, Measurement, and Control, 2017
The Kalman filter has a long history of use in input deconvolution where it is desired to estimate structured inputs or disturbances to a plant from noisy output measurements. However, little attention has been given to the convergence properties of the deconvolved signal, in particular the conditions needed to estimate inputs and disturbances with ...
Petschel, B. S., Naveh, K. Soltani, McAree, P. R.   +2 more
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On the convergence of the LMS algorithm in adaptive filtering

Signal Processing, 2004
An independence assumption on the input vectors is commonly used when stating the convergence of the least mean square algorithm is adaptive filtering. From this hypothesis a range in which the convergence factor must be chosen is determined. In this paper the independence assumption, unrealistic in the case of adaptive filtering, is avoided.
Maria Inés Troparevsky, Carlos E. D'Attellis   +1 more
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