Multichannel myopic deconvolution in underwater acoustic channels via low-rank recovery.
Multichannel myopic deconvolution in underwater acoustic channels via low-rank recovery.
复制标题
通过低秩恢复在水声通道中进行多通道近视反卷积。
DOI：
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发表时间：
2017
影响因子：
2.4
通讯作者：
J. Romberg
中科院分区：
文献类型：
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作者：
Ning Tian;S. Byun;K. Sabra;J. Romberg
本文介绍了一种解决多通道盲解析问题的技术。作者观察到具有K不同（未知）通道响应的单个（未知）源的卷积；从这些通道输出中，作者希望估算源和通道响应。作者展示了如何将这个经典信号处理问题视为求解双线性方程系统，并且可以重新铸造，因为从一组线性观测值中恢复了等级-1矩阵。在低级别基质恢复领域的先前研究结果已经确定了这种类型和有效，可扩展的启发式求解器问题的有效凸松弛，使这些技术能够与数千个未知变量一起使用。作者展示了如何使用有关渠道的先验信息来构建通道的线性模型，从而使这些方程式解决方程式解决方程式。这项研究证明了这种方法对通过几种风格化和浅水声通道模拟的通道脉冲响应的测量噪声和参数化误差的鲁棒性。还使用在短底部安装的垂直线阵列中记录的运输噪声对该方法的性能进行了实验验证。
This paper presents a technique for solving the multichannel blind deconvolution problem. The authors observe the convolution of a single (unknown) source with K different (unknown) channel responses; from these channel outputs, the authors want to estimate both the source and the channel responses. The authors show how this classical signal processing problem can be viewed as solving a system of bilinear equations, and in turn can be recast as recovering a rank-1 matrix from a set of linear observations. Results of prior studies in the area of low-rank matrix recovery have identified effective convex relaxations for problems of this type and efficient, scalable heuristic solvers that enable these techniques to work with thousands of unknown variables. The authors show how a priori information about the channels can be used to build a linear model for the channels, which in turn makes solving these systems of equations well-posed. This study demonstrates the robustness of this methodology to measurement noises and parametrization errors of the channel impulse responses with several stylized and shallow water acoustic channel simulations. The performance of this methodology is also verified experimentally using shipping noise recorded on short bottom-mounted vertical line arrays.