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An Adaptive Ultrasonic Backscattered Signal Processing Technique for Accurate Object Localization Based on the Instantaneous Energy Density Level

机译:基于瞬时能量密度水平的自适应超声后向散射信号处理技术,用于精确的目标定位

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摘要

In clinical medicine, Ultrasonics is utilized to acquire information of the human body tissues to perform diagnosis since it is convenient and nondestructive. In this article, an adaptive ultrasonic backscattered echoes processing technique for the accurate object localization based on the instantaneous energy density level (IE) is for the first time presented. In this technique, a series of intrinsic mode functions (IMFs) of the ultrasonic backscattered signal processed are acquired by the Ensemble Empirical Mode Decomposition (EEMD) algorithm firstly. And then the Hilbert transform (HT) is applied on IMFs screened by the IMF selection process to gain the Hilbert spectrum. Finally, the time-frequency information in the Hilbert spectrum is utilized to extract the instantaneous energy density level to detect and localize the objective. In this study, the proposed technique based on the IE shows its high-performance comparing with the classic envelope detection (CED) method, and the relative error of localization is no more than 1.8% even in strong noise environment (SNR = -10 dB).
机译:在临床医学中,超声波技术方便且无损,因此可用于获取人体组织信息以进行诊断。本文首次提出了一种基于瞬时能量密度水平(IE)的自适应超声反向散射回波处理技术,用于精确的目标定位。在该技术中,首先通过整体经验模态分解(EEMD)算法获取处理后的超声反向散射信号的一系列固有模式函数(IMF)。然后,将希尔伯特变换(HT)应用于通过IMF选择过程筛选的IMF,以获得希尔伯特频谱。最后,利用希尔伯特频谱中的时频信息提取瞬时能量密度水平,以检测和定位目标。在这项研究中,基于IE的拟议技术显示了其与经典包络检测(CED)方法相比的高性能,即使在强噪声环境下(SNR = -10 dB,定位的相对误差也不超过1.8% )。

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