【摘要】 针对水下目标噪声源定位问题，考虑声源的空间稀疏性，使用迭代收缩阈值弹性网正则化广义逆波束形成方法，实现噪声源的定位。根据声源的稀疏性引入L₁范数，获得L₁范数与广义逆波束形成相结合的目标函数，提出使用迭代收缩阈值算法求解该函数进而获取声源位置信息；由于仅有L₁范数时声源识别结果易受噪声影响，降低结果的稳健性，因此提出引入L₂范数，获得L₁范数和L₂范数共同约束的目标函数，即弹性网正则化广义逆波束形成，L₁范数和L₂范数可以保证收敛结果更稳健，然后将弹性网正则化广义逆波束形成视为类Lasso问题，并使用迭代收缩阈值算法求解进而获取声源位置信息；通过仿真和试验处理对比了迭代收缩阈值弹性网正则化广义逆波束形成与其他噪声源定位方法的性能，结果显示所提方法的噪声源定位精度和分辨率最高。更多还原

【Abstract】 In this paper, aiming at the problem of noise source location of underwater targets, considering the spatial sparsity of sound sources, the elastic network regularization generalized inverse beamforming with iterative shrinkage threshold is employed to realize the localization of the noise source. Firstly, the L₁ norm is introduced according to the sparsity of the sound source, and the objective function combining the L₁ norm with the generalized inverse beamforming is obtained. The iterative shrinkage threshold algorithm is proposed to solve the function and get the position information of the sound source. Secondly, sound source identification is easily affected by noise when there is only an L₁ norm, which reduces its robustness. Therefore, this paper proposes employing the L₂ norm to obtain the objective function jointly constrained by the L₁ norm and the L₂ norm, the elastic net regularized generalized inverse beamforming. The combination of the L₁ norm and the L₂ norm can ensure that the convergence result is more robust. Then the iterative shrinkage threshold algorithm is used to solve the elastic network regularized generalized inverse beamforming and obtain the position information of the sound source. Finally, the performance of the proposed method is compared with other noise source localization methods through simulation and experimental data processing. The proposed method has the highest noise source localization accuracy and resolution.更多还原