# Architecture Exploration of a Backprojection Algorithm for Real-Time Video SAR
###### Abstract  
This paper explores novel architectures for fast backprojection based video synthetic aperture radar (BP-VISAR) with multiple GPUs. The video SAR frame rate is analyzed for non-overlapped and overlapped aperture modes. For the parallelization of the backprojection process, a processing data unit is defined as the phase history data or range profile data from partial synthetic-apertures divided from the full resolution target data. Considering whether full-aperture processing is performed and range compression or backprojection are parallelized on a GPU basis, we propose six distinct architectures, each having a single-stream pipeline with a single GPU. The performance of these architectures is evaluated in both non-overlapped and overlapped modes. The efficiency of the BP-VISAR architecture with sub-aperture processing in the overlapped mode is accelerated further by filling the processing gap from the idling GPU resources with multi-stream based backprojection on multiple GPUs. The frame rate of the proposed BP-VISAR architecture with sub-aperture processing is scalable with the number of GPU devices for large pixel resolution. It can generate 4096\(\times\)4096 video SAR frames of 0.5 m cross-range resolution in 23.0 Hz on a single GPU and 73.5 Hz on quad GPUs. s 2021 21 8258 10.10.  
wide-area scenes. Frequency-domain algorithms resample the collected phase history into a rectangular raster after preprocessing and enable bulk operations using fast Fourier transform (FFT) to maximize computational efficiency. These algorithms store a chunk of data collected from the maximum scene size to process it in a frequency transform domain at the expense of marginal defocus [3; 4; 5]. Conventional SAR image formation for real-time processing has long been researched in terms of computational efficiencies and hardware accelerations. Still, their performances are limited by the increased scene sizes, particularly in a video SAR mode, where the amount of incoming raw data increases rapidly with a higher frame rate. In particular, the requirement for a sub-meter resolution level makes it difficult to implement in real-time. In addition, ground moving target detection is employed, but there is no general rule that can accommodate ground targets moving in arbitrary directions and velocities. In general, SAR images do not allow real-time tracking of moving objects or rapid change detection because they are not continuously produced in streams like SAR video frames. Recently, increased attention has focused on the video SAR system, which continuously senses a region of interest (ROI), while the moving radar platform operates either in flying by or circling the ROI in spotlight SAR modes [6; 7; 8; 9]. Video SAR sequences are constructed by placing video SAR frames generated in consecutive time order. In principle, the signal processing for video SAR shares the same algorithm with the conventional SAR image generation, but the synthetic aperture must be configured to adapt for multiple observations in consecutive time sequences. The video frame rate needs to be high enough to allow applications to visually track moving objects through a video SAR frame sequence, extracting information such as the velocities, moving tracks, types of objects, etc. In video SAR, it is highly challenging to handle the computational loads of generating video SAR frames in real time while observing dynamic scenes in designated configurations. Real-time video SAR processing is essential for day-and-night surveillance on collaborative video SAR sensor networks, which is another type of Internet of Things (IoT) [10; 11]. Recent studies on video SAR have focused on video SAR simulation [6; 7; 12; 13], frame rate analysis [12; 14; 13], and field experiments [9; 15; 16; 17]. All these studies construct a synthetic aperture for a video SAR frame in non-overlapped or overlapped aperture modes along the flight path of the platform.