Precise point positioning (PPP) approach is a good alternative to the differential positioning techniques. However, PPP requires a comparatively long initialization period. As both the pseudolite and PPP-GNSS estimated float ambiguities converge to their correct values with geometry change, the addition of pseudolite measurements could help shorten the convergence period. In order to overcome the signal blockage problem of the GNSS, the integration architecture and data fusion algorithm of PPP-GNSS, pseudolite, and INS are the main research of this chapter. A pseudolite-augmented PPP-GNSS solution strategy is introduced firstly, and then a loosely coupled architecture of PPP-GNSS, pseudolite, and INS is presented. A step further, three integration algorithms—centralized Kalman filtering (CKF), federated Kalman filtering (FKF), and global optimal filtering (GOF)—are introduced and implemented into a triple-integrated PPP-GNSS/pseudolite/INS system. Finally, the experiment and result analysis are presented.