Statistical Analysis Of Sar Signature Domains

In support of airborne radar detection missions that rely on Synthetic Aperture Radar (SAR) imagery, there is a need for extensive sets of training data. Due to a paucity of measured data from some targets of interest, there is sometimes a need to train on only simulated SAR data, and yet detect live targets with high confidence during testing. In support of this mission, many researchers have applied a variety of mathematical techniques to simulate data sets. These techniques range from template matching and simpler statistical methods to deep neural networks (DDNs). They demonstrate that with proper pre-processing, some of these methods can achieve target detection with apparently high confidence. However, for all these papers there is no exact measurement of the differences or similarities in the simulated and measured data that would provide a good predictor of the margins between decision boundaries. Thus, this paper has developed a combination of pre-processing methods and standard metrics that enable the assessment of simulated data quality independent of which target recognition algorithm will be utilized. The results show that for some pre-processing methods the differences in simulated data and measured data do not always lend themselves to the desired ability to train on simulated SAR imagery and test on measured SAR imagery.