Results 231 to 240 of about 4,548 (274)

Adaptive Kalman Filtering for INS/GPS

Journal of Geodesy, 1999
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Mohamed, A. H., Schwarz, K. P.
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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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Adaptive Kalman Filtering: A Simulation Result

Journal of Dynamic Systems, Measurement, and Control, 1988
This paper presents the algorithm for on-line adaptive Kalman filtering of sensor signals with unknown signal to noise ratio. A first order spectrum of a pure signal and white Gaussian measurement noise have been assumed. The results of the performance tests of the algorithm as well as the design methodology of the adaptive filter are given.
Sasiadek, J. Z., Wojcik, P. J.
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Adaptive fading Kalman filter with an application

Automatica, 1994
Abstract A new adaptive state estimation algorithm, namely adaptive fading Kalman filter (AFKF), is proposed to solve the divergence problem of Kalman filter. A criterion function is constructed to measure the optimality of Kalman filter. The forgetting factor in AFKF is adaptively adjusted by minimizing the defined criterion function using measured ...
Qijun Xia, Ming Rao, Yiqun Ying, Xuemin Shen   +3 more
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Distributed Kalman Filtering With Adaptive Communication

IEEE Control Systems Letters
This letter proposes an adaptive event-triggered communication framework for distributed state estimation in sensor networks, enabling each node to self-adapt its transmission rule while maintaining a desired average rate and complying with an upper bound on individual transmission rates.
Daniela Selvi, Giorgio Battistelli
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New adaptive Kalman filters using filter bank

2002 IEEE International Symposium on Circuits and Systems. Proceedings (Cat. No.02CH37353), 2003
The adaptive algorithms that update adaptive filter coefficients are important in adaptive signal processing. For the adaptive algorithms, the following are required: the reduction of the computational complexity, the simple hardware implementation and so on.
Ken Okuyama, Sadanobu Yoshimoto, Toshihiro Furukawa   +2 more
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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.
Alireza Moghaddamjoo, R. Lynn Kirlin
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An Adaptive Kalman Filter Bank for ECG Denoising

IEEE Journal of Biomedical and Health Informatics, 2021
Model-based Bayesian frameworks proved their effectiveness in the field of ECG processing. However, their performances rely heavily on the pre-defined models extracted from ECG signals. Furthermore, their performances decrease substantially when ECG signals do not comply with their models- a situation generally occurs in the case of arrhythmia-.
Hamed Danandeh Hesar, Maryam Mohebbi
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An Adaptive Iterated Kalman Filter

The Proceedings of the Multiconference on "Computational Engineering in Systems Applications", 2006
The recursive filtering of discrete-time nonlinear systems in the presence of unknown noise statistical parameters is studied. By embedding the modified Sage-Husa noise statistics estimator into the iterated Kalman filter, an adaptive iterated Kalman filter is obtained. With iterative operations as well as the online estimation of unknown covariance of
Yong-An Zhang, null Di Zhou, Guang-ren Duan   +2 more
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An adaptive neurofuzzy Kalman filter

Proceedings of IEEE 5th International Fuzzy Systems, 2002
It is of great practical significance to merge the neural network identification technique and the Kalman filter to achieve adaptive and optimal filtering and prediction for unknown observable nonlinear processes. In this paper, an operating point dependent ARMA model is used to represent the nonlinear system, and a neurofuzzy network is used to ...
null Zhi Qiao Wu, C.J. Harris
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