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中文核心期刊

利用低复杂度傅里叶积分法的水声无源弱目标探测方法

Underwater acoustic passive weak target detection using low-complexity Fourier integral method

  • 摘要: 针对多目标、低信噪比条件下无源线列阵对弱目标检测性能不足的问题, 提出了低复杂度自适应傅里叶积分法(LCA-FIM)。所提方法设计多个预设窗函数获得多个不同性能的加权傅里叶积分法输出, 利用低复杂度自适应窗函数优选获得最优输出, 从而获得高增益、低旁瓣效果, 并具有低计算量。仿真结果表明, LCA-FIM在低信噪比下具有良好的分辨性能和背景抑制能力。海试数据结果进一步表明, 与常规波束形成、自适应波束形成方法相比, LCA-FIM在提高分辨率的同时降低输出背景, 具有在多目标和低信噪比条件下进行弱目标检测的能力。

     

    Abstract: To address the insufficient weak target detection performance of passive linear array under multi-target and low signal-to-noise ratio (SNR), a low-complexity adaptive Fourier integral method (LCA-FIM) is proposed. The proposed method designs multiple predefined window functions to generate weighted Fourier integral method outputs with diverse performance characteristics. Through low-complexity adaptive window function optimization, the optimal output is selected to achieve high gain, low sidelobe, and reduced computational complexity. Simulation results demonstrate that the LCA-FIM achieves superior resolution performance and robust background suppression capability under low SNR. The experimental results further demonstrate that, compared with conventional beamforming and adaptive beamforming methods, the LCA-FIM enhances resolution while suppressing background noise, thereby exhibiting robust weak target detection capabilities under multi-target and low SNR.

     

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