  This paper is concerned with sequential state filtering in the presence of
nonlinearity, non-Gaussianity and model uncertainty. For this problem, the
Bayesian model averaged particle filter (BMAPF) is perhaps one of the most
efficient solutions. Major advances of BMAPF have been made, while it still
lacks a generic and practical approach to design the model set. This paper
fills in this gap by proposing a generic data-driven method for BMAPF model set
design. Unlike existent methods, the proposed solution does not require any
prior knowledge on the parameter value of the true model; it only assumes that
a small number of noisy observations are pre-obtained. The Bayesian
optimization (BO) method is adapted to search the model components, each of
which is associated with a specific segment of the pre-obtained dataset.The
average performance of these model components is guaranteed since each one's
parameter value is elaborately tuned via BO to maximize the marginal
likelihood. The diversity in the model components is also ensured, as different
components match the different segments of the pre-obtained dataset,
respectively. Computer simulations are used to demonstrate the effectiveness of
the proposed method.
