When a Synthetic Aperture Radar (SAR) acquires raw data using a satellite or airborne platform, it must be transferred to the ground for further processing. For example, SAR raw data need a so-called ’focusing’ signal processing to render it into a visible image. Such processing is time and computing consuming, and it is commonly carried out in computing centres. Since the data transfer rate is a typical limitation when communicating with the ground station, compression is necessary to reduce transmission time. So far, this procedure has been implemented in application-specific hardware, but recent adoption of avionic computational GPUs opened to new high-performance onboard perspectives. Due to the limited availability of avionic GPUs, we focused on parallel performance estimation starting from measures relative to a similar off-the-shelf solution. In this paper, we present a GPU algorithm for raw SAR data compression, which uses 1-dimensional DCT transforms, followed by quantisation and entropy coding. We evaluate results using ENVISAT (Environmental Satellite) ASAR Image Mode level 0 data by measuring compression rates, statistical parameters, and distortion on decompressed and then focused images. Moreover, by evaluating the Algorithmic Overhead induced by the parallelisation strategy, we predict the best thread-block configuration for possible adoption of such a GPU algorithm on one of the most available avionic hardware.

当合成孔径雷达（SAR）使用卫星或机载平台获取原始数据时，必须将其传输到地面进行进一步处理。例如，SAR原始数据需要进行所谓的“聚焦”信号处理，以将其渲染为可见图像。这样的处理是时间和计算量，并且通常在计算中心中进行。由于数据传输速率是与地面站通信时的典型限制，因此需要压缩以减少传输时间。到目前为止，此过程已在专用硬件中实现，但最近航空电子计算GPU的采用为新的高性能机载视角打开了大门。由于航空电子GPU的可用性有限，我们专注于并行性能评估，该评估从相对于类似现成解决方案的度量开始。在本文中，我们提出了一种用于原始SAR数据压缩的GPU算法，该算法使用一维DCT变换，然后进行量化和熵编码。我们使用ENVISAT（环境卫星）ASAR图像模式0级数据评估结果，方法是测量压缩率，统计参数和解压缩后聚焦图像的失真。此外，通过评估 以及解压缩后聚焦图像的失真。此外，通过评估 以及解压缩后聚焦图像的失真。此外，通过评估由并行化策略引起的算法开销，我们预测最佳线程块配置，以便在最可用的航空电子硬件之一上可能采用这种GPU算法。