基于改进CoSaMP的管道污垢超声检测信号的压缩重构研究

RESEARCH ON COMPRESSION AND RECONSTRUCTION OF TUBE FOULING ULTRASONIC TESTING SIGNAL BASED ON IMPROVED COSAMP

在超声检测中,传感器采集数据时易造成冗余信号和随机噪声,抑制随机噪声的同时压缩重构信号一直是超声检测领域的研究热点和难点。基于压缩感知理论和超声检测方法,提出一种改进压缩采样匹配追踪(Compressive Sampling Matching Pursuit,CoSaMP)的管道污垢超声检测信号压缩重构算法:采用K-SVD算法稀疏表示超声检测信号,推导峰值信噪比与迭代次数之间的关系,自适应确定稀疏度,利用回波信号与随机噪声在变换域的稀疏表征差异拾取有效信息。结果表明:该改进CoSaMP算法能准确定位信号稀疏度,重构超声检测信号反映了原始信号特征,且运算时间明显缩短,证明了改进CoSaMP算法重构超声检测信号的可行性。

In ultrasonic tests,the redundant signal and random noise are easily to be generated when the sensor collects data.Compressing and reconstructing the signal or noise always get the researchers’ attention.Based on the theory of compressive sensing and ultrasonic testing method,an improved CoSaMP for tube fouling ultrasonic testing compression and reconstruction was proposed:The K-SVD algorithm was used to sparsely represent the ultrasonic testing signal.The relationship between the peak signal to noise ratio and the number of iterations was derived to determine the sparse degree adaptively.Finally,the sparsity differences of the echo signal and random noise in the transform domain was used to pick up effective information.The results showed that the improved CoSaMP algorithm can accurately locate signal sparsity.The reconstructed signal reflects that the characteristics of the original signal and the operation time are shorter.That proves the feasibility of the improved CoSaMP algorithm.