  The set-membership information fusion problem is investigated for general
multisensor nonlinear dynamic systems. Compared with linear dynamic systems and
point estimation fusion in mean squared error sense, it is a more challenging
nonconvex optimization problem. Usually, to solve this problem, people try to
find an efficient or heuristic fusion algorithm. It is no doubt that an
analytical fusion formula should be much significant for rasing accuracy and
reducing computational burden. However, since it is a more complicated than the
convex quadratic optimization problem for linear point estimation fusion, it is
not easy to get the analytical fusion formula. In order to overcome the
difficulty of this problem, two popular fusion architectures are considered:
centralized and distributed set-membership information fusion. Firstly, both of
them can be converted into a semidefinite programming problem which can be
efficiently computed, respectively. Secondly, their analytical solutions can be
derived surprisingly by using decoupling technique. It is very interesting that
they are quite similar in form to the classic information filter. In the two
analytical fusion formulae, the information of each sensor can be clearly
characterized, and the knowledge of the correlation among measurement noises
across sensors are not required. Finally, multi-algorithm fusion is used to
minimize the size of the state bounding ellipsoid by complementary advantages
of multiple parallel algorithms. A typical numerical example in target tracking
demonstrates the effectiveness of the centralized, distributed, and
multi-algorithm set-membership fusion algorithms. In particular, it shows that
multi-algorithm fusion performs better than the centralized and distributed
fusion.
