  The decentralized particle filter (DPF) was proposed recently to increase the
level of parallelism of particle filtering. Given a decomposition of the state
space into two nested sets of variables, the DPF uses a particle filter to
sample the first set and then conditions on this sample to generate a set of
samples for the second set of variables. The DPF can be understood as a variant
of the popular Rao-Blackwellized particle filter (RBPF), where the second step
is carried out using Monte Carlo approximations instead of analytical
inference. As a result, the range of applications of the DPF is broader than
the one for the RBPF. In this paper, we improve the DPF in two ways. First, we
derive a Monte Carlo approximation of the optimal proposal distribution and,
consequently, design and implement a more efficient look-ahead DPF. Although
the decentralized filters were initially designed to capitalize on parallel
implementation, we show that the look-ahead DPF can outperform the standard
particle filter even on a single machine. Second, we propose the use of bandit
algorithms to automatically configure the state space decomposition of the DPF.
