  Estimation techniques to precisely localize a kinematic platform with GNSS
observables can be broadly partitioned into two categories: differential, or
undifferenced. The differential techniques (e.g., real-time kinematic (RTK))
have several attractive properties, such as correlated error mitigation and
fast convergence; however, to support a differential processing scheme, an
infrastructure of reference stations within a proximity of the platform must be
in place to construct observation corrections. This infrastructure requirement
makes differential processing techniques infeasible in many locations. To
mitigate the need for additional receivers within proximity of the platform,
the precise point positioning (PPP) method utilizes accurate orbit and clock
models to localize the platform. The autonomy of PPP from local reference
stations make it an attractive processing scheme for several applications;
however, a current disadvantage of PPP is the slow positioning convergence when
compared to differential techniques. In this paper, we evaluate the convergence
properties of PPP with an incremental graph optimization scheme (Incremental
Smoothing and Mapping (iSAM2)), which allows for real-time filtering and
smoothing. The characterization is first conducted through a Monte Carlo
analysis within a simulation environment, which allows for the variations of
parameters, such as atmospheric conditions, satellite geometry, and intensity
of multipath. Then, an example collected data set is utilized to validate the
trends presented in the simulation study.
