Radar backscatter from the sea-surface is typically non-stationary and may contain sea-spikes which resemble targets and can cause false detections in a radar system. To improve detection performance, this paper investigates a technique based on stationary wavelet transforms (SWT) and demonstrates its performance on both monostatic and bistatic sea-clutter data. By exploiting the different components (sub-bands) of the SWT decomposition, this technique can better distinguish between the different spectral components of the returned signal. The detection performance is measured by Monte Carlo simulation with a cell averaging constant false alarm rate (CA-CFAR) detector. The most appropriate choice of sub-band is selected using an a entropy based metric
