Relevant bibliographies by topics / Minimum variance filtering

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Minimum variance filtering'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Minimum variance filtering"

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Shaked, U., and C. E. de Souza. "Robust minimum variance filtering." IEEE Transactions on Signal Processing 43, no. 11 (1995): 2474–83. http://dx.doi.org/10.1109/78.482099.

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Theodor, Y., and U. Shaked. "Robust discrete-time minimum-variance filtering." IEEE Transactions on Signal Processing 44, no. 2 (1996): 181–89. http://dx.doi.org/10.1109/78.485915.

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Buckley, K. "Spatial/Spectral filtering with linearly constrained minimum variance beamformers." IEEE Transactions on Acoustics, Speech, and Signal Processing 35, no. 3 (1987): 249–66. http://dx.doi.org/10.1109/tassp.1987.1165142.

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Jetto, L. "Three dimensional minimum variance recursive filtering with implicit spatiotemporal compensation." IFAC Proceedings Volumes 32, no. 2 (1999): 3850–55. http://dx.doi.org/10.1016/s1474-6670(17)56657-4.

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Qi, Wen-Juan, Peng Zhang, and Zi-Li Deng. "Weighted Fusion Robust Steady-State Kalman Filters for Multisensor System with Uncertain Noise Variances." Journal of Applied Mathematics 2014 (2014): 1–11. http://dx.doi.org/10.1155/2014/369252.

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A direct approach of designing weighted fusion robust steady-state Kalman filters with uncertain noise variances is presented. Based on the steady-state Kalman filtering theory, using the minimax robust estimation principle and the unbiased linear minimum variance (ULMV) optimal estimation rule, the six robust weighted fusion steady-state Kalman filters are designed based on the worst-case conservative system with the conservative upper bounds of noise variances. The actual filtering error variances of each fuser are guaranteed to have a minimal upper bound for all admissible uncertainties of
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Grosse-Wentrup, Moritz, and Martin Buss. "Overcomplete Independent Component Analysis via Linearly Constrained Minimum Variance Spatial Filtering." Journal of VLSI Signal Processing Systems for Signal, Image, and Video Technology 48, no. 1-2 (2007): 161–71. http://dx.doi.org/10.1007/s11265-006-0028-3.

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Rykaczewski, Krzysztof, Jan Nikadon, Włodzisław Duch, and Tomasz Piotrowski. "supFunSim: Spatial Filtering Toolbox for EEG." Neuroinformatics 19, no. 1 (2020): 107–25. http://dx.doi.org/10.1007/s12021-020-09464-w.

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AbstractBrain activity pattern recognition from EEG or MEG signal analysis is one of the most important method in cognitive neuroscience. The supFunSim library is a new Matlab toolbox which generates accurate EEG forward model and implements a collection of spatial filters for EEG source reconstruction, including the linearly constrained minimum-variance (LCMV), eigenspace LCMV, nulling (NL), and minimum-variance pseudo-unbiased reduced-rank (MV-PURE) filters in various versions. It also enables source-level directed connectivity analysis using partial directed coherence (PDC) measure. The sup
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Van Veen, B. D., W. Van Drongelen, M. Yuchtman, and A. Suzuki. "Localization of brain electrical activity via linearly constrained minimum variance spatial filtering." IEEE Transactions on Biomedical Engineering 44, no. 9 (1997): 867–80. http://dx.doi.org/10.1109/10.623056.

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Medina S., César A., and Raimundo Sampaio-Neto. "An inverse QRD-RLS algorithm for linearly constrained minimum variance adaptive filtering." Signal Processing 93, no. 5 (2013): 1308–16. http://dx.doi.org/10.1016/j.sigpro.2012.11.002.

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Tan, Xiu Hu. "An Information Fusion Algorithm Based on Kalman Filtering." Applied Mechanics and Materials 444-445 (October 2013): 1072–76. http://dx.doi.org/10.4028/www.scientific.net/amm.444-445.1072.

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For the multisensor systems with unknown noise variances, by the statistics method, the mathematical model and the noise statistics are essential, and this limitation was settled by adaptive algorithm. The adaptive Kalman filter was proposed to solve the filtering problem of the system with unknown mathematical model or noise statistics in information fusion. Based on the probability method and the scalar weighting optimal information fusion criterion in the minimum variance sense, the algorithm can not only optimize the multi-channel data, but also obtain the minimum mean square error (MMSE)
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Dissertations / Theses on the topic "Minimum variance filtering"

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Woodbury, Drew Patton. "Accounting for Parameter Uncertainty in Reduced-Order Static and Dynamic Systems." Thesis, 2011. http://hdl.handle.net/1969.1/ETD-TAMU-2011-12-10220.

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Parametric uncertainty is one of many possible causes of divergence for the Kalman filter. Frequently, state estimation errors caused by imperfect model parameters are reduced by including the uncertain parameters as states (i.e., augmenting the state vector). For many situations, this not only improves the state estimates, but also improves the accuracy and precision of the parameters themselves. Unfortunately, not all filters benefit from this augmentation due to computational restrictions or because the parameters are poorly observable. A parameter with low observability (e.g., a set of hig
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Book chapters on the topic "Minimum variance filtering"

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Liang, Jinling, Zidong Wang, and Fan Wang. "Minimum-Variance Recursive Filtering for 2-D Systems with Degraded Measurements: Boundedness and Monotonicity." In Recursive Filtering for 2-D Shift-Varying Systems with Communication Constraints. CRC Press, 2021. http://dx.doi.org/10.1201/9781003189213-2.

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Einicke, Garry. "Continuous-Time Minimum-Variance Filtering." In Smoothing, Filtering and Prediction - Estimating The Past, Present and Future. InTech, 2012. http://dx.doi.org/10.5772/39251.

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Einicke, Garry. "Discrete-Time Minimum-Variance Prediction and Filtering." In Smoothing, Filtering and Prediction - Estimating The Past, Present and Future. InTech, 2012. http://dx.doi.org/10.5772/39252.

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Einicke, Garry. "Discrete-Time Steady-State Minimum-Variance Prediction and Filtering." In Smoothing, Filtering and Prediction - Estimating The Past, Present and Future. InTech, 2012. http://dx.doi.org/10.5772/39253.

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KERR, THOMAS H. "Computational Techniques for the Matrix Pseudoinverse in Minimum Variance Reduced-Order Filtering and Control." In Control and Dynamic Systems. Elsevier, 1988. http://dx.doi.org/10.1016/b978-0-12-012728-3.50005-6.

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Conference papers on the topic "Minimum variance filtering"

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Palanthandalam-Madapusi, Harish J., and Dennis S. Bernstein. "Unbiased Minimum-variance Filtering for Input Reconstruction." In 2007 American Control Conference. IEEE, 2007. http://dx.doi.org/10.1109/acc.2007.4282834.

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Satyanarayana, Neeli, and S. Janardhanan. "Minimum variance functional filtering for multirate sampled systems." In 2013 International Conference on Control, Computing, Communication and Materials (ICCCCM). IEEE, 2013. http://dx.doi.org/10.1109/iccccm.2013.6648906.

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Fitch, Katherine E., and Harish J. Palanthandalam-Madapusi. "Unbiased minimum-variance filtering for delayed input reconstruction." In 2011 American Control Conference. IEEE, 2011. http://dx.doi.org/10.1109/acc.2011.5991542.

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Jian Liu, Jian Liang Wang, and Guang-Hong Yang. "Reliable robust minimum variance filtering with sensor failures." In Proceedings of American Control Conference. IEEE, 2001. http://dx.doi.org/10.1109/acc.2001.945858.

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Chien-Shu Hsieh. "Optimal Minimum-Variance Filtering for Systems with Unknown Inputs." In 2006 6th World Congress on Intelligent Control and Automation. IEEE, 2006. http://dx.doi.org/10.1109/wcica.2006.1712679.

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Hsieh, Chien-Shu. "Robust Parametrized Minimum-Variance Filtering for Uncertain Systems with Unknown Inputs." In 2007 American Control Conference. IEEE, 2007. http://dx.doi.org/10.1109/acc.2007.4283059.

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Zhang, Yilian, Fuwen Yang, and Qing-Long Han. "Unbiased minimum-variance filtering for systems with randomly multi-step sensor delays." In IECON 2014 - 40th Annual Conference of the IEEE Industrial Electronics Society. IEEE, 2014. http://dx.doi.org/10.1109/iecon.2014.7049053.

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Chang, Hong, and Zuren Feng. "A Method of Data Fusion Based on the Robust Minimum Variance Filtering." In 2007 IEEE International Conference on Control and Automation. IEEE, 2007. http://dx.doi.org/10.1109/icca.2007.4376663.

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Hsieh, Chien-Shu. "Optimal Filtering for Systems with Unknown Inputs Via Unbiased Minimum-Variance Estimation." In TENCON 2006 - 2006 IEEE Region 10 Conference. IEEE, 2006. http://dx.doi.org/10.1109/tencon.2006.344113.

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Yang, Chen, and Huajing Fang. "Gain-constrained minimum variance filtering with multiple packet dropouts and stochastic nonlinearities." In 2016 35th Chinese Control Conference (CCC). IEEE, 2016. http://dx.doi.org/10.1109/chicc.2016.7554510.

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