School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, USA.
Department of Electrical and Computer Engineering, The Ohio State University, Columbus, Ohio 43210, USA.
J Acoust Soc Am. 2020 Oct;148(4):2267. doi: 10.1121/10.0001975.
A general blind deconvolution algorithmic framework is developed for sources of opportunity (e.g., ships at known locations) in an ocean waveguide. Here, both channel impulse responses (CIRs) and unknown source signals need to be simultaneously estimated from only the recorded signals on a receiver array using blind deconvolution, which is generally an ill-posed problem without any a priori information or additional assumptions about the underlying structure of the CIRs. By exploiting the typical ray-like arrival-time structure of the CIRs between a surface source and the elements of a vertical line array (VLA) in ocean waveguides, a principle component analysis technique is applied to build a bilinear parametric model linking the amplitudes and arrival-times of the CIRs across all channels for a variety of admissible ocean environments. The bilinear channel representation further reduces the dimension of the channel parametric model compared to linear models. A truncated power interaction deconvolution algorithm is then developed by applying the bilinear channel model to the traditional subspace deconvolution method. Numerical and experimental results demonstrate the robustness of this blind deconvolution methodology.
针对海洋波导中的机会源（例如已知位置的船只），开发了一种通用的盲反卷积算法框架。在此，仅使用接收器阵列上记录的信号，通过盲反卷积同时估计信道冲激响应 (CIR) 和未知源信号，这通常是一个不适定问题，没有关于 CIR 底层结构的任何先验信息或附加假设。通过利用海洋波导中表面源与垂直线阵列 (VLA) 元素之间 CIR 的典型射线状到达时间结构，应用主成分分析技术构建了一种双线性参数模型，该模型将各种可接受的海洋环境下所有通道的 CIR 的幅度和到达时间联系起来。与线性模型相比，双线性信道表示进一步降低了信道参数模型的维度。然后，通过将双线性信道模型应用于传统的子空间反卷积方法，开发了一种截断功率互作用反卷积算法。数值和实验结果证明了这种盲反卷积方法的稳健性。
