GPS/INS integration via faded memory Kalman filter

A common technique for navigation and positioning applications is the Global Positioning System (GPS)/Inertial Navigation System (INS) integration, which combines the strengths of GPS and INS to offer accurate and reliable information. As a standalone system, the performance of the INS deteriorates as time is passed. Kalman Filter (KF) is used for GPS/INS integration, and its performance is excellent for simple data. However, in a complex and natural set environment, its performance degrades when the system performs relatively long; therefore, resolving the long-time problem for the GPS/INS system is challenging. The novelty of this paper is GPS/INS integration with the Faded Kalman Filter (FKF). In the FKF, the measurement updates are weighted differently to adapt to changes in the system. This approach allows the filter to adapt to changes or uncertainties in the system dynamics. GPS/INS integration performance is significantly improved using this algorithm rather than a simple KF. An average of 45% reduces the positioning errors compared to traditional KF.