  This paper proposes a new method for RTK post-processing. Different from the
traditional forward-backward Kalman filter, in our method, the whole system
equation is built on a graphical state space model and solved by factor graph
optimization. The position solution provided by the forward Kalman filter is
used as the linearization points of the graphical state space model. Constant
variables, such as double-difference ambiguity, will exist as constants in the
graphical state space model, not as time-series variables. It is shown by
experiment results that factor graph optimization with a graphical state space
model is more effective than Kalman filter with a traditional discrete-time
state space model for RTK post-processing problem.
