Most existing DOA estimation methods assume ideal source incident angles with minimal noise. Moreover, directly using pre-estimated angles to calculate weighted coefficients can lead to performance loss. Thus, a green multi-modal (MM) fusion DOA framework is proposed to realize a more practical, low-cost and high time-efficiency DOA estimation for a H$^2$AD array. Firstly, two more efficient clustering methods, global maximum cos\_similarity clustering (GMaxCS) and global minimum distance clustering (GMinD), are presented to infer more precise true solutions from the candidate solution sets. Based on this, an iteration weighted fusion (IWF)-based method is introduced to iteratively update weighted fusion coefficients and the clustering center of the true solution classes by using the estimated values. Particularly, the coarse DOA calculated by fully digital (FD) subarray, serves as the initial cluster center. The above process yields two methods called MM-IWF-GMaxCS and MM-IWF-GMinD. To further provide a higher-accuracy DOA estimation, a fusion network (fusionNet) is proposed to aggregate the inferred two-part true angles and thus generates two effective approaches called MM-fusionNet-GMaxCS and MM-fusionNet-GMinD. The simulation outcomes show the proposed four approaches can achieve the ideal DOA performance and the CRLB. Meanwhile, proposed MM-fusionNet-GMaxCS and MM-fusionNet-GMinD exhibit superior DOA performance compared to MM-IWF-GMaxCS and MM-IWF-GMinD, especially in extremely-low SNR range.

翻译：现有大多数DOA估计方法假设理想的信号入射角度且噪声极小。此外，直接使用预估计角度计算加权系数可能导致性能损失。为此，本文提出一种绿色多模态融合DOA框架，旨在为H$^2$AD阵列实现更实用、低成本且高时效的DOA估计。首先，提出了两种更高效的聚类方法——全局最大余弦相似度聚类与全局最小距离聚类，以从候选解集中推断更精确的真实解。基于此，引入一种基于迭代加权融合的方法，利用估计值迭代更新加权融合系数及真实解类的聚类中心。特别地，由全数字子阵列计算的粗粒度DOA将作为初始聚类中心。上述过程产生了两种方法：MM-IWF-GMaxCS与MM-IWF-GMinD。为进一步提供更高精度的DOA估计，提出一种融合网络以聚合推断出的两部分真实角度，从而形成两种有效方法：MM-fusionNet-GMaxCS与MM-fusionNet-GMinD。仿真结果表明，所提出的四种方法均能达到理想的DOA性能与克拉美-罗下界。同时，相较于MM-IWF-GMaxCS与MM-IWF-GMinD，所提出的MM-fusionNet-GMaxCS与MM-fusionNet-GMinD展现出更优越的DOA性能，尤其在极低信噪比区间表现显著。