Adaptive Detection and Removal of Non-Gaussian Spikes from Gaussian Data
Rachel Boucher, J.P. Noonan · IEEE Transactions on Pattern Analysis and Machine Intelligence · 1982
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.