The Underwater Acoustic Image Measurement Based on Non-uniform Spatial Resampling RL Deconvolution

Jidan Mei, Yuqing Pei, Chao Ma, Yunfei Lv, Qiuying Peng
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Abstract

When the near-field underwater acoustic image (UAI) measurement is carried out by the line array laid on the sea floor, the resolution of the conventional beamforming (CBF) acoustic image measurement method is poor, the sidelobe level is high, while the deconvolution algorithm has the effect of high resolution and low sidelobe. However, the direct deconvolution algorithm of the point spread function (PSF) shift-variant model has a large computational burden. This paper presents a non-uniform spatial resampling Richardson-Lucy (RL) fast algorithm, which based on energy distribution of conventional acoustic image measurement result make the original uniform space scanning to non-uniform spatial resampling. It can reduce the number of scanning grid, so as to reduce the amount of computation. Simulation results show that the fast RL algorithm can achieve the performance close to the original RL algorithm by reducing the computational amount by nearly an order of magnitude.
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基于非均匀空间重采样RL反卷积的水声图像测量
在海底布设线阵进行近场水声图像测量时,传统波束形成(CBF)声图像测量方法分辨率较差,旁瓣电平较高,而反卷积算法具有高分辨率、低旁瓣的效果。然而,点扩散函数(PSF)位移变模型的直接反卷积算法计算量很大。本文提出了一种非均匀空间重采样Richardson-Lucy (RL)快速算法,该算法基于常规声图像测量结果的能量分布,使原均匀空间扫描变为非均匀空间重采样。它可以减少扫描网格的数量,从而减少计算量。仿真结果表明,快速强化学习算法通过减少近一个数量级的计算量,可以达到接近原始强化学习算法的性能。
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