Bootstrap methods for adaptive signal detection

A general bootstrap procedure for signal detection is presented. Two methods under this general procedure are given. One method requires a regression model to be available to generate the bootstrap data while the other method assumes a pivot. Examples of detecting known signals, signals with unknown parameters and random signals are given. Performance in terms of false alarm and detection rates of the bootstrap methods are found using simulations and compared with the performance of the constant false alarm rate (CFAR) matched filter, the CFAR subspace matched filter bank and a test for zero skewness. Results in an application using ground penetrating radar data to detect buried landmines are also presented