文章摘要
沈晓炜.基于粒子群算法的稀疏阵列超声相控阵全聚焦成像[J].,2020,39(3):354-359
基于粒子群算法的稀疏阵列超声相控阵全聚焦成像
Ultrasonic sparse-TFM imaging using particle swarm optimization algorithm
投稿时间:2019-07-19  修订日期:2020-04-28
中文摘要:
      研究了一种用于稀疏阵列全聚焦成像的阵列优化算法。针对目前超声相控阵检测全矩阵采集数据量大,全聚焦算法成像时间长的难点,本文通过构建稀疏阵列,在满足成像质量的同时显著降低数据量并提高成像效率。通过以主瓣宽度、旁瓣峰值以及主瓣峰值作为约束条件构建适应度函数,采用粒子群算法得到稀疏阵元位置分布并进行阵元权重修正,并将其用于稀疏全聚焦成像。相比全阵元成像,使用粒子群算法所得的稀疏阵列减少了65.62%的阵元个数,数据使用量降低了88.18%。在阵列优化方面,相比遗传算法减少了84.86%的计算时间
英文摘要:
      An efficient array optimization algorithm for ultrasonic sparse-total focusing method (TFM) imaging is studied. Aiming at the problem of a huge amount of full matrix data and long imaging time of TFM imaging in the ultrasonic phased array inspection, this paper constructs a sparse array to reduce the data and improve the imaging efficiency while assuring the image quality. By using the main lobe width, the side lobe peak and the main lobe peak as the constraints to construct the fitness function, the particle swarm optimization (PSO) algorithm is used to obtain the sparse array elements position distribution and the matrix weight correction, and they are used for sparse-TFM imaging. Compare imaging with full elements of an array, the sparse array obtained by particle swarm optimization algorithm reduces the number of array elements by 65.62%,and the data usage is reduced by 88.18%. In terms of array optimization, the computation time is reduced by 84.86% compared to the genetic algorithm.
DOI:10.11684/j.issn.1000-310X.2020.03.005
中文关键词: 稀疏阵列,粒子群算法,相控阵超声检测,全聚焦算法
英文关键词: Sparse array, Particle swarm optimization, Phased array ultrasonic testing ,Total focusing method
基金项目:
作者单位E-mail
沈晓炜 长沙理工大学 长沙 2791172337@qq.com 
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