基于粒子群算法的稀疏阵列超声相控阵全聚焦成像

为降低相控阵超声检测全聚焦算法的成像数据量及阵列稀疏优化的计算时间,研究了一种用于稀疏阵列全聚焦成像的阵列优化算法,并通过实验对其成像效果进行了验证。针对目前超声相控阵检测的全矩阵采集数据量大、全聚焦算法成像时间长的难点,该文通过构建稀疏阵列,在保证成像质量的同时显著降低成像数据量,提高了全聚焦算法的成像效率。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的阵元个数降低了56.25%、65.62%,数据使用量降低了80.86%、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。

Ultrasonic sparse-TFM imaging using particle swarm optimization algorithm

To reduce the amount of imaging data of the total focusing method in phased array ultrasonic detection and computing time for array sparse optimization,an array optimization algorithm for full focusing sparse array imaging is studied and its imaging results are verified by experiments.Aiming at the problem of a huge amount of full matrix data and long imaging time of total focusing method(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 56.25% and 65.62%,respectively,and the data usage is reduced by 80.86% and 88.18%,respectively.In terms of array optimization,the computation time is reduced by 84.86% compared to the genetic algorithm.