  Recent years have bore witness to the proliferation of distributed filtering
techniques, where a collection of agents communicating over an ad-hoc network
aim to collaboratively estimate and track the state of a system. These
techniques form the enabling technology of modern multi-agent systems and have
gained great importance in the engineering community. Although most distributed
filtering techniques come with a set of stability and convergence criteria, the
conditions imposed are found to be unnecessarily restrictive. The paradigm of
stability and convergence in distributed filtering is revised in this
manuscript. Accordingly, a general distributed filter is constructed and its
estimation error dynamics is formulated. The conducted analysis demonstrates
that conditions for achieving stable filtering operations are the same as those
required in the centralized filtering setting. Finally, the concepts are
demonstrated in a Kalman filtering framework and validated using simulation
examples.

Signal Processing;Systems and Control;Systems and Control