This paper describes the integration of Locata,GNSS and INS technologies within a loosely-coupled triple integration algorithm.The conventional methods for multi-sensor integrationcan be classified as eithercentralised filtering or decentralised filtering.Centralised Kalmanfiltering (CKF) provides globally optimal state estimation by directly combining measurement data.However CKF system has some disadvantages such as a comparatively large computational burden and poor fault detection and isolation ability.DecentralisedKalman filtering (DKF)addresses such defects whileaiming to achieve the same accuracy as acentralised filter.On the other hand global optimal filtering (GOF) can achieve a higher accuracy than the traditional CKFbecauseit utilises more information resources than the CKF.In the information space,the information resources that can be used for estimation includethe measurements,the local predictions,and the global predictions.In order to evaluate the system performance,a field experiment was conducted on a vehicle with different kinds of maneuvres,including circular motion and accelerated motion.The resultsindicate that: (1) GOF-based PPP-GNSS/Locata/INS integration system can provide better positioning accuracy compared with CFK and federated Kalman filtering; (2) covariance analysis shows that the GOF improves the system estimation covariance; and (3) acomparison of GOF with local filters confirms the superiority of a GOF-based triple integration system.