The sound source identification of elastic network regularized generalized inverse beamforming based on iterative shrinkage thresholding

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.