  In this paper, we study a class of stochastic optimal control problem with
jumps under partial information. More precisely, the controlled systems are
described by a fully coupled nonlinear multi- dimensional forward-backward
stochastic differential equation driven by a Poisson random measure and an
independent multi-dimensional Brownian motion, and all admissible control
processes are required to be adapted to a given subfiltration of the filtration
generated by the underlying Poisson random measure and Brownian motion. For
this type of partial information stochastic optimal control problem, we give a
necessary and sufficient maximum principle. All the coefficients appearing in
the systems are allowed to depend on the control variables and the control
domain is convex.
