Results 251 to 260 of about 39,931 (297)

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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Kalman filter for adaptive antennas

Wuhan University Journal of Natural Sciences, 1998
Adaptive control algorithm is a key technique for an adaptive array. It is necessary to find a fast and efficient algorithm for gaining rapid interference suppression. Convergence speed of conventional gradient-based algorithms is extremely slow and very sensitive to eigenvalue spread of autocorrelation matrix.
Hu, Jiayuan, Zhong, Haibin
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Adaptive suboptimal Kalman filtering

The 22nd IEEE Conference on Decision and Control, 1983
Adaptive Kalman filtering has attracted a lot of attention during the last 15 years; perusal of the published literature shows the diversity of the techniques that have been proposed in this context. In this note, we present an adaptive Kalman filtering scheme, based on the quantization of the parameter space; this quantization is performed in order to
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Combined adaptive robust Kalman filter algorithm

Measurement Science and Technology, 2021
Abstract The precise positioning of dynamic and static objects such as vehicles and pedestrians is a key technology. A global navigation satellite system signal is the primary signal source required to achieve precise positioning, and the optimal estimation method used in precise positioning is Kalman filtering (KF).
Xu Lin, Wei Li, Shaoda Li, Jiang Ye, Chaolong Yao, Zhijie He   +5 more
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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.
K. Okuyama, S. Yoshimoto, T. Furukawa
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Distributed Kalman Filtering With Adaptive Communication

IEEE Control Systems Letters
This work 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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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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