  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.
