Distributed algorithms, particularly Diffusion Least Mean Square, 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.

翻译：分布式算法，尤其是扩散最小均方算法，因其在各类工业应用中表现出的可靠性、鲁棒性和快速收敛性而广受青睐。然而，目标的可观测性受限可能危及算法的完整性。为解决这一问题，本文借鉴信号流分析的思想，提出了一种分析组合策略的框架。同时，提出了一种基于阈值的算法，用于在目标向量支撑信息缺失的情况下识别并利用支撑向量。所提方法在两种组合场景中得到验证，展示了该算法在时域和变换域稀疏观测情况下的有效性。