We present a novel method of improving signal-to-noise ratio in radargrams. The method uses singular spectrum analysis (SSA) to separate each individual radar trace into orthogonal components. The components that explain most of the original trace variance contain mainly physically meaningful signal, while the components with little variance tend to be noise. Adding the largest-magnitude components together until the sum of components accounts for the variance above the noise level (typically 60-80% %) of the original trace variance results in a much cleaner radargram with more easily seen internal features than in traditionally filtered data. The radargrams can be further enhanced by envelope-detecting the SSA-filtered data, as this measure of instantaneous energy minimizes the deleterious effects of innumerable phase changes at dielectric boundaries. Subsequent incoherent stacking results in far more structured radargrams than are achieved with traditionally processed radar data and amplitude stacking.