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Horizontal Wavenumber Estimation Technique Based on Compressive Sensing in Shallow Water *

机译:基于压缩感测的浅水波数估计技术*

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The horizontal wavenumber of normal mode contains environmental information of ocean waveguide, which is an important environmental sensing parameter. To solve the problem of large array aperture requirement and poor resolution performance of the existing horizontal array wavenumber spectrum estimation methods, a horizontal wavenumber estimation technique based on sparse processing is proposed. On the basis of normal mode theory in shallow water, the sparsity of horizontal wavenumber in shallow water is utilized. For the case of coherent and incoherent processing of multiple snapshot data, Basis Pursuit De-Noising(BPDN) and Sparse Bayesian Learning(SBL) methods are used to estimate the horizontal wavenumber. The resolution of horizontal wavenumber spectrum with Sparse Bayesian Learning method is verified to be best through simulation.
机译:正常模式的水平波数包含海洋波导的环境信息,这是一个重要的环境感知参数。为解决现有水平阵列波数谱估计方法对阵列孔径要求大,分辨率性能差的问题,提出了一种基于稀疏处理的水平波数估计技术。根据浅水波的模态理论,利用了浅水波数的稀疏性。对于多个快照数据的相干和不相干处理,使用了基本追踪去噪(BPDN)和稀疏贝叶斯学习(SBL)方法来估计水平波数。通过仿真,证明了利用稀疏贝叶斯学习方法对水平波数谱的分辨率是最好的。

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