【摘要】 为了提高解卷积常规波束形成(D-CBF)算法的稳定性,降低方位谱背景噪声级,提高处理增益,提出了一种基于全变分约束的解卷积常规波束形成(TVD-CBF)空间谱估计算法。该方法利用声源分布的稀疏先验,在代价函数中加入总变分正则化项作为非线性约束,获得TVD-CBF算法的方位谱,从而在提升空间分辨率的同时,抑制噪声及误差的累积,提高求解的稳定性。仿真表明,该方法在D-CBF算法的基础上进一步提升了方位指向性和分辨率,具有良好的波达方向估计性能。海试数据处理结果表明, TVD-CBF方法在提升空间分辨率的同时,降低了空间谱的背景级,具有良好的方位估计性能。更多还原

【Abstract】 In order to enhance the stability of the deconvolved conventional beamforming(D-CBF) algorithm, reduce background noise levels in the azimuth spectrum, and improve processing gain, a total variation constrained deconvolved CBF(TVD-CBF)spatial spectrum estimation algorithm is proposed. The approach leverages the sparse prior of the source distribution by incorporating a total variation regularization term as a nonlinear constraint within the cost function. Consequently, spatial resolution is improved while the accumulation of noise and errors is suppressed, thereby enhancing solution stability. Simulation results demonstrate that the TVD-CBF algorithm significantly outperforms the D-CBF algorithm in terms of azimuthal directivity and resolution, exhibiting surperior performance in direction of arrival estimation. The effectiveness of the TVD-CBF algorithm is further validated through experiments on sea trial data.更多还原