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Compressed sensing for wideband wavenumber tracking in dispersive shallow water

机译:压缩感知用于离散浅水宽带波数跟踪

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In shallow water zones and at low frequency, seabed and water column properties can be estimated from the acoustic wavenumbers using inversion algorithms. When considering horizontal line arrays (HLA) and narrowband sources, the wavenumbers can be evaluated with classic spectral analysis methods. In this paper, a compressed sensing (CS) method for sparse recovery of the wavenumbers is proposed. This takes advantage of the few propagating modes and allows for spectral estimation when short HLA are used. The CS representation improves the wavenumber estimation, compared to the Fourier transform. However, for small arrays and several propagating modes, the CS generates interferences and does not allow proper wavenumber estimation. When considering broadband sources, it is possible to combine the wavenumbers estimated at several frequencies in order to build a frequency-wavenumber (f - k) representation. In this case, a post-processing tracking operation which improves the f - k resolution is presented. This relies on a general approach of waveguide physics and uses a particle filtering (PF) algorithm to track the wavenumbers. The consecutive use of CS and PF leads to a better wavenumber estimation. This methodology can be used for sources that are not at an end-fire position. It is illustrated by simulations and successfully applied on the Shallow Water 2006 data using the 32 sensor SHARK array. (C) 2015 Acoustical Society of America.
机译:在浅水区和低频下,可以使用反演算法根据声波数估算海床和水柱的特性。当考虑水平线阵列(HLA)和窄带源时,可以使用经典的频谱分析方法评估波数。提出了一种稀疏恢复波数的压缩感知(CS)方法。这利用了少数传播模式,并在使用短HLA时允许频谱估计。与傅立叶变换相比,CS表示改进了波数估计。但是,对于小阵列和几种传播模式,CS会产生干扰,并且无法正确估计波数。当考虑宽带源时,可以组合在几个频率处估计的波数,以建立频率-波数(f-k)表示。在这种情况下,提出了提高f-k分辨率的后处理跟踪操作。这依赖于波导物理学的一般方法,并使用粒子滤波(PF)算法来跟踪波数。 CS和PF的连续使用导致更好的波数估计。该方法可用于不在末火位置的源。通过仿真进行了说明,并使用32个传感器SHARK阵列成功应用于浅水2006数据。 (C)2015年美国声学学会。

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