模式过滤与一个小的水平线阵列*

Yifeng Zhang, Jinjin Wang, Guolong Liang, Zhibo Shi
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引用次数: 0

摘要

模式滤波是被动定位或源深度识别的关键步骤。由于水平线阵列(HLA)在深度方向上缺少模式采样,因此无法利用模式的正交性进行模式滤波。因此,HLA的模式过滤,特别是小HLA的模式过滤通常是一个难题。本文研究了小HLA的模式滤波方法,该方法可应用于小HLA的被动定位或源深度识别。首先,用正态模态模型描述了HLA上的压力,并介绍了几种经典的模态滤波器。然后考虑到模态幅值与波数对应,将模态滤波看作是在波数域中进行的广义波束形成。通过约束最大旁瓣最小值,对模态振幅进行波束形成。最后,在蒙特卡罗仿真中对该滤波器的性能进行了评价。
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Mode Filtering with a Small Horizontal Line Array*
Mode filtering is a key procedure in passive localization or source depth discrimination. Since the horizontal line array (HLA) lacks sampling of modes in depth direction, the orthogonality of modes can't be utilized in mode filtering with a HLA. Thus, the mode filtering with a HLA, especially with a small HLA is generally a difficult problem. In this paper, we focus on the mode filtering with a small HLA, which can be applied to the passive localization or source depth discrimination with a small HLA. Firstly, the pressure received on the HLA is describe by normal mode model, and several classical mode filters are introduced. Then considering the modal amplitudes are corresponding to the wavenumbers, we regard the mode filtering as generalized beamforming, which is performed in the wavenumber domain. Through constraining the maximum side lobe minimum, the modal amplitudes are beamformed. Finally, the performance of the proposed mode filter is evaluated relative to other methods in Monte Carlo simulation.
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