  Distributed signal processing algorithms have become a hot topic during the
past years. One class of algorithms that have received special attention are
particles filters (PFs). However, most distributed PFs involve various
heuristic or simplifying approximations and, as a consequence, classical
convergence theorems for standard PFs do not hold for their distributed
counterparts. In this paper, we analyze a distributed PF based on the
non-proportional weight-allocation scheme of Bolic {\em et al} (2005) and prove
rigorously that, under certain stability assumptions, its asymptotic
convergence is guaranteed uniformly over time, in such a way that approximation
errors can be kept bounded with a fixed computational budget. To illustrate the
theoretical findings, we carry out computer simulations for a target tracking
problem. The numerical results show that the distributed PF has a negligible
performance loss (compared to a centralized filter) for this problem and enable
us to empirically validate the key assumptions of the analysis.
