GPU Processing for UAS-Based LFM-CW Stripmap SAR

Unmanned air systems (UAS) provide an excellent platform for synthetic aperture radar (SAR), enabling surveillance and research over areas too difficult, dangerous, or costly to reach using manned aircraft. However, the nimble nature of the small UAS makes them more susceptible to external forces, thus requiring significant motion compensation in order for SAR images to focus properly. SAR backprojection has been found to improve the focusing of low-altitude stripmap SAR images compared to frequency domain algorithms. In this paper we describe the development and implementation of SAR backprojection appropriate for UAS based stripmap SAR that utilizes the unique architecture of a GPU in order to produce high-quality imagery in real-time. Introduction Unmanned air systems (UAS) carrying synthetic aperture radar (SAR) can obtain high-quality high-resolution information over areas too difficult, dangerous, or costly to reach using manned aircraft. SAR systems are active radars that transmit and receive microwave signals. The received signals are used to create images of the surface and are able to operate regardless of illumination or weather conditions. The nimble nature of a small UAS makes it much more mobile but also more susceptible to external forces, thus requiring significant motion compensation in order for SAR images to focus properly. Lowaltitude operation further complicates motion compensation of stripmap SAR images, due to the large range of incidence angles and increased range cell migration. SAR backprojection inherently handles arbitrary aircraft motion and low-altitude geometry and can form images directly along known topography making it a particularly effective algorithm for UAS-based SAR. However, backprojection is much more computationally demanding than frequency domain algorithms (Melvin and Scheer, 2012). Fortunately, backprojection processing is easily parallelized and computed efficiently on graphics processing units (GPU). Several studies have been conducted on implementing backprojection on GPUs, most notably Fasih and Hartley (2010), Benson et al., (2012), Capozzoli et al. (2013), and Nguyen et al. (2004), but these papers focus on the simplest form of spotlight mode SAR backprojection and are not directly applicable to stripmap SAR. In this paper we present the development and implementation of a highly efficient GPU-based SAR backprojection processor for stripmap imaging. In particular, we develop a stripmap processor for linear frequency-modulated continuous-wave (LFM-CW) SAR systems operated on a UAS. This paper is organized as follows. We begin with a background discussing the LFM-CW signal, stripmap SAR, and a brief introduction to the NVIDIA GPU architecture and Compute Unified Device Architecture (CUDA). Then, we develop a SAR backprojection method that accounts for motion during the pulse and the moving antenna pattern, which is suitable for UAS based stripmap SAR. This is followed by a discussion of the implementation of the SAR processor on a GPU. Finally, we use SAR data from CASIE 2009 (Long et al., 2010) to analyze the performance of the implementation and present the resulting imagery.