Using Dual Function Radar Communication Waveforms for Synthetic Aperture Radar Automatic Target Recognition

As machine learning (ML), artificial intelligence (AI), and deep learning continue to advance, their applications become more diverse – one such application is synthetic aperture radar (SAR) automatic target recognition (ATR). These SAR ATR networks use different forms of deep learning such as convolutional neural networks (CNN) to classify targets in SAR imagery. An emerging research area of SAR is dual function radar communication (DFRC) which performs both radar and communications functions using a single co-designed modulation. The utilization of DFRC emissions for SAR imaging impacts image quality, thereby influencing SAR ATR network training. Here, using the Civilian Vehicle Data Dome dataset from the AFRL, SAR ATR networks are trained and evaluated with simulated data generated using Gaussian Minimum Shift Keying (GMSK) and Linear Frequency Modulation (LFM) waveforms. The networks are used to compare how the target classification accuracy of the ATR network differ between DFRC (i.e., GMSK) and baseline (i.e., LFM) emissions. Furthermore, as is common in pulse-agile transmission structures, an effect known as ’range sidelobe modulation’ is examined, along with its impact on SAR ATR. Finally, it is shown that SAR ATR network can be trained for GMSK emissions using existing LFM datasets via two types of data augmentation.