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Filtering of Noisy Time-Series Data

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If you have a question about this talk, please contact Shakir Mohamed.

The aim of this talk is to provide some intuition and the basic theory of some of the most common filtering methods used in signal processing. We provide illustrative examples and run some live demos.

The Kalman filter, pp. 19-24, in http://www.cs.unc.edu/tracker/media/pdf/SIGGRAPH2001_CoursePack_08.pdf

The UKF http://www.cs.unc.edu/welch/kalman/media/pdf/Julier1997_SPIE_KF.pdf

The PHD filter http://www.ee.cuhk.edu.hk/~wkma/publications/gmphd_2col.pdf

This talk is part of the Machine Learning Reading Group @ CUED series.

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