Results 281 to 290 of about 75,448 (316)
Some of the next articles are maybe not open access.
Information Sciences, 1998
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Guanrong Chen +2 more
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Guanrong Chen +2 more
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DIVERGENCE IN THE KALMAN FILTER
Guidance and Control Conference, 1966Under certain conditions, the orbit estimated by a Kalman filter has errors that are much greater than predicted by theory. This phenomenon is called divergence, and renders the operation of the Kalman filter unsatisfactory. This paper investigates the control of divergence in a Kalman filter used for autonomous navigation in a low earth orbit.
F. H. SCHLEE, C. J. STANDISH, N. F. TODA
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International Journal of Control, 1997
For an implicitly defined discrete system, a new algorithm for Kalman filtering is developed and an efficient numerical implementation scheme is proposed. Unlike the traditional explicit approach, the implicit filter can be readily applied to ill-conditioned systems and allows for generalization to descriptor systems. The implementation of the implicit
M, Skliar, W F, Ramirez
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For an implicitly defined discrete system, a new algorithm for Kalman filtering is developed and an efficient numerical implementation scheme is proposed. Unlike the traditional explicit approach, the implicit filter can be readily applied to ill-conditioned systems and allows for generalization to descriptor systems. The implementation of the implicit
M, Skliar, W F, Ramirez
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2015 IEEE China Summit and International Conference on Signal and Information Processing (ChinaSIP), 2015
In this work, a sparse Kalman filter (SKF) exploring the signal sparse property is developed to track unknown time-varying signals. To derive SKF, the measurement update in KF is reformulated into a convex optimization problem first, and then a regularization term l 1 -norm on parameters of interest is introduced to yield sparse estimates.
Hongqing Liu +3 more
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In this work, a sparse Kalman filter (SKF) exploring the signal sparse property is developed to track unknown time-varying signals. To derive SKF, the measurement update in KF is reformulated into a convex optimization problem first, and then a regularization term l 1 -norm on parameters of interest is introduced to yield sparse estimates.
Hongqing Liu +3 more
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2018 IEEE/ION Position, Location and Navigation Symposium (PLANS), 2018
The Extended Kalman Filter Is a proven method for efficient Markov Chain inference. It is ubiquitous in indoor localization applications, and typically applied to combine relative motion with absolute positioning. However, an unmodified Extended Kalman Filter struggles to handle common problems in indoor applications.
Boxian Dong +2 more
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The Extended Kalman Filter Is a proven method for efficient Markov Chain inference. It is ubiquitous in indoor localization applications, and typically applied to combine relative motion with absolute positioning. However, an unmodified Extended Kalman Filter struggles to handle common problems in indoor applications.
Boxian Dong +2 more
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2007 IEEE 22nd International Symposium on Intelligent Control, 2007
This paper is concerned with the estimation problem for a dynamic stochastic estimation in a sensor network. Firstly, the quantized Kalman filter based on the quantized observations (QKFQO) is presented. Approximate solutions for two optimal bandwidth scheduling problems are given, where the tradeoff between the number of quantization levels or the ...
Shuli Sun +3 more
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This paper is concerned with the estimation problem for a dynamic stochastic estimation in a sensor network. Firstly, the quantized Kalman filter based on the quantized observations (QKFQO) is presented. Approximate solutions for two optimal bandwidth scheduling problems are given, where the tradeoff between the number of quantization levels or the ...
Shuli Sun +3 more
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Kalman filter and quantization
Problems of Information Transmission, 2007zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Proceedings of the 2000 American Control Conference. ACC (IEEE Cat. No.00CH36334), 2000
Infinite-horizon Kalman filtering is re-examined and generalized to include a class of nonstationary and nonergodic disturbances. This revision is achieved by defining a generalized infinite-horizon filtering problem using a flexible functional analytic signal description. It is shown that the solution to the generalized filtering problem is equivalent
Pertti M. Makila, J. Paattilammi
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Infinite-horizon Kalman filtering is re-examined and generalized to include a class of nonstationary and nonergodic disturbances. This revision is achieved by defining a generalized infinite-horizon filtering problem using a flexible functional analytic signal description. It is shown that the solution to the generalized filtering problem is equivalent
Pertti M. Makila, J. Paattilammi
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IEEE Transactions on Signal Processing, 1994
A modified Kalman filtering algorithm is described. The key point of the new algorithm is a model mismatch function, which accounts for deviation of the model from the ideal condition of orthogonality between the innovations process and past observations. >
Simon Haykin 0001, Liang Li
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A modified Kalman filtering algorithm is described. The key point of the new algorithm is a model mismatch function, which accounts for deviation of the model from the ideal condition of orthogonality between the innovations process and past observations. >
Simon Haykin 0001, Liang Li
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2017
Damit Kalman-Filter korrekt eingesetzt werden konnen, ist es wichtig, die Randbedingen zu kennen, unter der die Kalman-Gleichungen verwendet werden durfen. Dies bedeutet, dass die jeweilig zu losende Aufgabe dahingehend zu uberprufen ist. Sind diese Voraussetzungen nicht gegeben, liefern die Kalman-Gleichungen nicht das gewunschte Ergebnis.
Reiner Marchthaler, Sebastian Dingler
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Damit Kalman-Filter korrekt eingesetzt werden konnen, ist es wichtig, die Randbedingen zu kennen, unter der die Kalman-Gleichungen verwendet werden durfen. Dies bedeutet, dass die jeweilig zu losende Aufgabe dahingehend zu uberprufen ist. Sind diese Voraussetzungen nicht gegeben, liefern die Kalman-Gleichungen nicht das gewunschte Ergebnis.
Reiner Marchthaler, Sebastian Dingler
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