  This paper studies the convergence conditions and properties of the
distributed adaptive signal fusion (DASF) algorithm, the framework itself
having been introduced in a `Part I' companion paper. The DASF algorithm can be
used to solve linear signal and feature fusion optimization problems in a
distributed fashion, and is in particular well-suited for solving spatial
filtering optimization problems encountered in wireless sensor networks. The
convergence conditions and results are provided along with rigorous proofs and
analyses, as well as various example problems to which they apply.
Additionally, we describe procedures that can be added to the DASF algorithm to
ensure convergence in specific cases where some of the technical convergence
conditions are not satisfied.
