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Time delay estimation using new spectral and adaptive filtering methods with applications to underwater target detection.

机译：使用新的频谱和自适应滤波方法进行时延估计，并应用于水下目标检测。

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摘要

In this dissertation, we present several novel approaches for detection and identification of targets of arbitrary shapes from the acoustic backscattered data and using the incident waveform. This problem is formulated as time-delay estimation and sinusoidal frequency estimation problems which both have applications in many other important areas in signal processing. Solving time-delay estimation problem allows the identification of the specular components in the backscattered signal from elastic and non-elastic targets. Thus, accurate estimation of these time delays would help in determining the existence of certain clues for detecting targets. Several new methods for solving these two problems in the time, frequency and wavelet domains are developed.;In the time domain, a new block fast transversal filter (BFTF) is proposed for a fast implementation of the least squares (LS) method. This BFTF algorithm is derived by using data-related constrained block-LS cost function to guarantee global optimality. The new soft-constrained algorithm provides an efficient way of transferring weight information between blocks of data and thus it is computationally very efficient compared with other LS-based schemes. Additionally, the tracking ability of the algorithm can be controlled by varying the block length and/or a soft constrained parameter. The effectiveness of this algorithm is tested on several underwater acoustic backscattered data for elastic targets and non-elastic (cement chunk) objects.;In the frequency domain, the time-delay estimation problem is converted to a sinusoidal frequency estimation problem by using the discrete Fourier transform. Then, the lagged sample covariance matrices of the resulting signal are computed and studied in terms of their eigen-structure. These matrices are shown to be robust and effective in extracting bases for the signal and noise subspaces. New MUSIC and matrix pencil-based methods are derived these subspaces. The effectiveness of the method is demonstrated on the problem of detection of multiple specular components in the acoustic backscattered data.;Finally, a method for the estimation of time delays using wavelet decomposition is derived. The sub-band adaptive filtering uses discrete wavelet transform for multi-resolution or sub-band decomposition. Joint time delay estimation for identifying multi-specular components and subsequent adaptive filtering processes are performed on the signal in each sub-band. This would provide multiple "look" of the signal at different resolution scale which results in more accurate estimates for delays associated with the specular components. Simulation results on the simulated and real shallow water data are provided which show the promise of this new scheme for target detection in a heavy cluttered environment.
机译：在本文中，我们提出了几种新颖的方法，用于从声反向散射数据和使用入射波形检测和识别任意形状的目标。这个问题被表述为时延估计和正弦频率估计问题，它们在信号处理的许多其他重要领域都有应用。解决时延估计问题可以识别来自弹性和非弹性目标的后向散射信号中的镜面反射分量。因此，对这些时间延迟的准确估计将有助于确定用于检测目标的某些线索的存在。提出了几种在时域，频域和小波域中解决这两个问题的新方法。在时域中，为快速实现最小二乘（LS）方法，提出了一种新的块快速横向滤波器（BFTF）。该BFTF算法是通过使用与数据相关的约束LS代价函数来保证全局最优性而派生的。新的软约束算法提供了一种在数据块之间传输权重信息的有效方法，因此与其他基于LS的方案相比，该算法在计算上非常有效。另外，可以通过改变块长度和/或软约束参数来控制算法的跟踪能力。在弹性目标和非弹性（水泥块）目标的多个水下声反向散射数据上测试了该算法的有效性。在频域中，通过使用离散量将时延估计问题转换为正弦频率估计问题傅里叶变换。然后，根据信号的本征结构计算并研究所得信号的滞后样本协方差矩阵。这些矩阵在提取信号和噪声子空间的基数方面表现出强大和有效的作用。这些子空间派生出新的基于MUSIC和矩阵铅笔的方法。在检测声反向散射数据中多个镜面反射分量的问题上证明了该方法的有效性。最后，推导了一种基于小波分解的时延估计方法。子带自适应滤波使用离散小波变换进行多分辨率或子带分解。对每个子带中的信号执行用于识别多镜面分量的联合时延估计和后续的自适应滤波过程。这将以不同的分辨率比例提供信号的多个“外观”，从而导致对与镜面反射分量相关的延迟进行更准确的估计。提供了对模拟和真实浅水数据的仿真结果，表明了在杂乱无章的环境中使用该新方案进行目标检测的前景。

著录项

  • 作者

    Hasan, Mohammed A.;

  • 作者单位

    Colorado State University.;

  • 授予单位 Colorado State University.;
  • 学科 Engineering Electronics and Electrical.;Physics Acoustics.
  • 学位 Ph.D.
  • 年度 1997
  • 页码 149 p.
  • 总页数 149
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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