Nonparametric methods for clutter removal

Methods of clutter rejection are discussed which furnish an inherent counte rpart of target tracking and detection algorithms. We describe how nonparam etric curve estimation methods reduce the original sensor data to a "signal -plus-noise" model which is well suited for various hypotheses testing and dynamical filtering algorithms. We also verify a "white noise" assumption f or the model of residuals.