异构环境下的多子阵合成孔径声呐精确后向投影快速成像方法

A Fast Accurate Back-Projection Algorithm for Multi-receiver Synthetic Aperture Sonar in Heterogeneous Environment

  • 摘要: 针对多子阵合成孔径声呐精确后向投影成像算法效率低的问题, 提出了一种异构环境下的精确多子阵合成孔径声呐后向投影成像快速方法。在分析精确逐点后向投影成像算法原理的基础上, 将脉冲压缩和方位向聚焦过程改造为单指令多线程模式, 借助图形处理器(graphics processor unit, GPU)强大的多核计算能力加速成像过程。通过仿真和实测数据的成像实验验证了所提快速成像算法的正确性和高效性, 与串行成像算法相比, 其加速比分别高达326.3和333.6。对于大规模数据成像处理, 所提方法体现出优异的加速性能, 满足实时信号处理需求, 同时为后续开展运动补偿奠定了基础。

     

    Abstract:
      Objectives  Synthetic aperture sonar (SAS) is one kind of high resolution underwater imaging sonar. Its principle is to use small array to simulate large aperture array by uniforming linear motion along the direction of flight track, and realize coherent processing of echo signal to obtain high resolution two-dimensional sonar image, which is independent of imaging distance and acoustic wavelength. The back projection algorithm is an accurate point-by-point imaging algorithm, which is characterized by accurate focusing ability, suitable for wide beam and large scene imaging, and easy to realize motion compensation, but it has the disadvantages of low computational efficiency. In recent years, with the development of computer technology, especially the appearance of graphics processor unit, it has powerful computing power and provides a new way to accelerate the back projection algorithm.
      Methods  We propose a fast accurate back projection algorithm for multi-receiver SAS in heterogeneous environment. On the basis of analyzing the principle of accurate back-projection imaging algorithm, the pulse compression and time-consuming azimuth accumulation are transformed into a single instruction multi-threaded mode, and the imaging process is accelerated by the powerful multi-core computing ability of graphics processor unit.
      Results  The validity and efficiency of the proposed fast imaging algorithm are verified by the imaging experiments performed on simulated and real SAS data. Compared with the serial imaging algorithm, its acceleration ratio is as high as 326.3 and 333.6. For large-scale data imaging processing, it shows excellent acceleration performance.
      Conclusions  The proposed back projection imaging algorithm greatly improves the imaging efficiency and meets the needs of real-time SAS imaging, which lays the foundation for subsequent motion compensation.

     

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