© 2018 Institute of Physics Publishing. All rights reserved.Extraction of distinguishing features and decision of classifiers are highly influenced by the low signal-to-noise ratio (SNR) rates, when target identification from scattered electromagnetic waves is considered. In order to increase the correct identification rates, smoothing operations, which should increase the SNR without greatly distorting the signal, are occasionally employed. However, this operation is mostly performed to the complete scattered signal via an over-complete basis. On the other hand, Savitzky-Golay filters can de-noise the signal by fitting successive sub-sets of adjacent scattered data points with a low-degree polynomial through the use of linear least squares. Thus in this study, both computational burden and accuracy of Savitzky-Golay filters are compared to three well-established smoothing techniques in time domain, frequency domain and time-frequency analysis. The analyses are performed with both simulated and measured data from various conductor and dielectric targets having different size, geometry and material type.