Title :
Adaptive Detection and Removal of Non-Gaussian Spikes from Gaussian Data
Author :
Boucher, R.E. ; Noonan, J.P.
Author_Institution :
Bedford Research Associates, Bedford, MA 01730.
fDate :
3/1/1982 12:00:00 AM
Abstract :
A nonlinear adaptive method is presented for filtering a signal which is corrupted by spikes which take discrete values Mi with probability Pi at random points in time. An unsupervised learning technique is used to estimate the unknown parameters Mi, Pi, and oi. The spikes are then removed using a Bayes classifier. A theoretical and experimental comparison with the MMSE linear filter is presented.
Keywords :
Adaptive filters; Circuit noise; Digital filters; Filtering; Gaussian noise; Hardware; Kalman filters; Noise cancellation; Nonlinear filters; Signal processing; Bayes classifier; Poisson process; maximum likelihood estimation; minimum mean-square error linear filter; mixture density; noise cancellation; unsupervised learning;
Journal_Title :
Pattern Analysis and Machine Intelligence, IEEE Transactions on
DOI :
10.1109/TPAMI.1982.4767218
