Distributed algorithms, particularly Diffusion LMS (DLMS), are widely favored for their reliability, robustness, and fast convergence in various industries. However, limited observability of the target can compromise the integrity of the algorithm. To address this issue, this paper proposes a framework for analyzing combination strategies by drawing inspiration from signal flow analysis. A thresholding-based algorithm is also presented to identify and utilize the support vector in scenarios with missing information about the target vector's support. The proposed approach is demonstrated in two combination scenarios, showcasing the effectiveness of the algorithm in situations characterized by sparse observations in the time and transform domains. The paper concludes with a discussion of the results obtained and avenues for further exploration.

翻译：分布式算法，特别是扩散最小均方（Diffusion LMS，DLMS）算法，因其在工业应用中具有可靠性、鲁棒性和快速收敛性而广受青睐。然而，目标观测的有限性可能损害算法的完整性。针对这一问题，本文借鉴信号流分析的思想，提出了一种用于分析组合策略的框架。同时，提出了一种基于阈值的算法，以在目标向量支持信息缺失的场景中识别并利用支持向量。所提方法在两种组合场景中进行了验证，展示了该算法在时域和变换域中稀疏观测情况下的有效性。本文最后对所得结果进行了讨论，并指出了进一步探索的方向。