Abstract
Synthetic Aperture Radar (SAR) raw data simulation is a fundamental problem in radar system design and imaging algorithm research. The growth of surveying swath and resolution results in a significant increase in data volume and simulation period, which can be considered to be a comprehensive data intensive and computing intensive issue. Although several high performance computing (HPC) methods have demonstrated their potential for accelerating simulation, the input/output (I/O) bottleneck of huge raw data has not been eased. In this paper, we propose a cloud computing based SAR raw data simulation algorithm, which employs the MapReduce model to accelerate the raw data computing and the Hadoop distributed file system (HDFS) for fast I/O access. The MapReduce model is designed for the irregular parallel accumulation of raw data simulation, which greatly reduces the parallel efficiency of graphics processing unit (GPU) based simulation methods. In addition, three kinds of optimization strategies are put forward from the aspects of programming model, HDFS configuration and scheduling. The experimental results show that the cloud computing based algorithm achieves speedup over the baseline serial approach in an 8-node cloud environment, and each optimization strategy can improve about 20%. This work proves that the proposed cloud algorithm is capable of solving the computing intensive and data intensive issues in SAR raw data simulation, and is easily extended to large scale computing to achieve higher acceleration.
Highlights
Due to the imaging characteristics of high resolution, day-and-night and weather-independent, Synthetic Aperture Radar (SAR) has been widely used for Earth remote sensing for more than 30 years, and it has come to play a significant role in geographical surveys, climate change research, environment and Earth system monitoring, multi-dimensional mapping and other applications [1]
The forward processing algorithms simulate the physical process of microwave transmitting and receiving, and calculate the SAR raw data, including the Sensors 2017, 17, 113; doi:10.3390/s17010113
In order to make the remote sensing from scientific research to industry, and bring more extensive applications, we propose a cloud computing based SAR
Summary
Due to the imaging characteristics of high resolution, day-and-night and weather-independent, Synthetic Aperture Radar (SAR) has been widely used for Earth remote sensing for more than 30 years, and it has come to play a significant role in geographical surveys, climate change research, environment and Earth system monitoring, multi-dimensional mapping and other applications [1]. Due to the time consuming and high-cost of SAR flight experiments, computer simulation is often applied to assist the key technology research, system design, system development, and even the data applications. In order to fulfill aforementioned support, the accurate and reliable raw data that contain various actual system errors and simulate large areas are necessary. The requirement poses a challenge for SAR raw data simulation accuracy and efficiency. The SAR raw data simulation algorithm can be mainly divided into two categories: forward processing and inverse processing. The forward processing algorithms simulate the physical process of microwave transmitting and receiving, and calculate the SAR raw data, including the Sensors 2017, 17, 113; doi:10.3390/s17010113 www.mdpi.com/journal/sensors
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