  We present a Bayesian data fusion method to approximate a posterior
distribution from an ensemble of particle estimates that only have access to
subsets of the data. Our approach relies on approximate probabilistic inference
of model parameters through Monte Carlo methods, followed by an update and
resample scheme related to multiple importance sampling to combine information
from the initial estimates. We show the method is convergent in the particle
limit and directly suited to application on multi-sensor data fusion problems
by demonstrating efficacy on a multi-sensor Keplerian orbit determination
problem and a bearings-only tracking problem.
