Optimal Data Fusion Algorithm for Navigation Using Triple Integration of PPP-GNSS, INS, and Terrestrial Ranging System

A good port management system must be able to perform safe, predictable, and efficient execution of transport processes. In order to improve the quality of the port management, a robust navigation system is required, which enables to provide the positions of vessels 24/7 on either open or impeded environment. This paper describes a robust georeferencing system that could satisfy centimeter-level accuracy requirements in port environments. The design is based on the loosely coupled integration of global navigation satellite system (GNSS) technology, a terrestrial radio frequency ranging system known as Locata, and an inertial navigation system (INS). GNSS observations are processed using the precise point positioning (PPP) approach instead of the conventional differential approach. To satisfy both accuracy and reliability requirements, three integration algorithms-centralized Kalman filtering (CKF), federated Kalman filtering (FKF), and global optimal filtering (GOF)-are investigated and implemented into a triple-integrated PPP-GNSS/Locata/INS system. A preliminary performance assessment, which is based on the analysis of real data, concludes that all the three integration algorithms are able to provide centimeter-level positioning solutions. The results show that the FKF and CKF algorithms have similar performance, whereas the GOF solution has higher accuracy. Moreover, the outlier simulation is conducted and the result verifies the outlier fault-tolerant capability of the GOF-based PPP-GNSS/Locata/INS integrated system.

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