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Echo parameter estimation for ultrasonic NDE applications via a two-step compressed sensing

机译:通过两步压缩感测来估算超声NDE应用的回声参数

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The recently developed compressed sensing (CS) framework has been applied to various data intensive imaging applications. It provides significant reductions in sampling rate with minimal loss in image quality. In this investigation, a CS-based sampling scheme is introduced for ultrasonic nondestructive evaluation (NDE) signals. The feasibility of parameter estimation based on the new sampling method is explored. Especially, a two-step compressed sensing with Gaussian chirplet model is proposed for data acquisition and echo parameter estimation. The CS framework is utilized to estimate the time-of-arrivals (TOAs) of chirplets. With the estimated TOAs, another iteration of data acquisition is performed to acquire the echo amplitude accurately. It has been shown that the two-step CS can estimate the TOAs of ultrasonic echoes with high accuracy in the presence of noise with SNR as low as -5 dB. Furthermore, the estimation is robust to inaccurate knowledge of the transducer bandwidth.
机译:最近开发的压缩感测(CS)框架已应用于各种数据密集型成像应用程序。它显着降低了采样率,同时图像质量损失最小。在这项研究中,引入了基于CS的采样方案,用于超声无损评估(NDE)信号。探索了基于新采样方法的参数估计的可行性。特别是,提出了一种基于高斯Chirplet模型的两步压缩感知技术,用于数据采集和回波参数估计。 CS框架用于估计of的到达时间(TOA)。利用估计的TOA,执行数据采集的另一次迭代以准确地获取回波幅度。已经表明,在存在SNR低至-5 dB的噪声的情况下,两步CS可以高精度估计超声回波的TOA。此外,该估计对于不准确地了解换能器带宽是鲁棒的。

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