Compressed Sensing for Radar Signature Analysis

Fast and accurate reproduction of the radar signature of a target is important for many radar applications, such as target identification and hardware-in-the-loop simulations. Real-world targets are electrically large, and the Nyquist criteria require fine sampling in frequency and angle to fully characterize the radar signature. Because of the fine sampling requirement, acquiring the radar signature is time and memory intensive. Even though fine sampling is required in the frequency and angle domains, most targets are sparse in a point scatter domain. This paper shows that the Nyquist criteria can be overcome through compressed sensing theory by leveraging the point scatter representation of a target. This greatly reduces the amount of data needed to characterize the radar signature for these defense applications.