Reinforced Ultra-Tightly Coupled GPS/INS System for Challenging Environment

Abstract

Among all integration levels currently available for Global Positioning System (GPS) and Inertial Navigation System (INS) Integrated System, ultra-tightly coupled (UTC) GPS/INS system is the best choice for accurate and reliable navigation. Nevertheless the performance of UTC GPS/INS system degrades in challenging environments, such as jamming, changing noise of GPS signals, and high dynamic maneuvers. When low-end Inertial Measurement Units (IMUs) based on MEMS sensors are employed, the performance degradation will be more severe. To solve this problem, a reinforced UTC GPS/INS system is proposed. Two techniques are adopted to deal with jamming and high dynamics. Firstly, adaptive integration Kalman filter (IKF) based on fuzzy logics is developed to reinforce the antijamming ability. The parameters of membership functions (MFs) are adjusted and optimized through self-developed neutral network. Secondly, a Doppler frequency error estimator based on Kalman filter is designed to improve the navigation performance under high dynamics. A complete simulation platform is established to evaluate the reinforced system. Results demonstrate that the proposed system architecture significantly improves navigation performance in challenging environments and it is a more advanced solution to accurate and reliable navigation than traditional UTC GPS/INS system.

A Novel IGC Scheme for RHV with the Capabilities of Online Aerodynamic Coefficient Estimation and Trajectory Generation

“…There are many cutting edge techniques for RHVs and one of them is guidance, navigation and control (GNC). Authors have conducted research on high-accuracy integrated navigation technologies for RHVs [3,4] and this paper focuses on integrated guidance and control. The mathematical foundation of re-entry guidance and control is the optimal solution for tightly coupled, high-rank nonlinear problems with complicated constraints under disturbances and uncertainties.…”

On the Use of an Ultra-Tight Integration for Robust Navigation in Jammed Scenarios

“…When low-end Inertial Measurement Units (IMUs) based on MEMS sensors are employed, the performance degradation will be even more severe. To solve this problem, two possible options can be adopted to reinforce the U-TC system: the first one is to use an adaptive Kalman filter (AKF), as done in [13], where the covariance matrixes of process noise (i.e., Q) and measurement noise (i.e., R) of the KF are continuously adapted according to the estimated noise characteristics of the surrounding environment. The second approach does not change the design of the KF but it reinforces the traditional U-TC with measurements coming from additional sensors (e.g.…”

“…Second, although significant effort has been spent on developing visual odometry and attitude algorithms (in particular through a monocular camera), the efficiency and performance regarding image frame rate has yet to be evaluated. Eventually, the integrated system is validated by using a live dataset differently from most of the previous works on the ultra-tight integration, that tested their algorithm only via simulation [11][12][13].…”