A Novel Design for the Ultra-Tightly Coupled GPS/INS Navigation System

Abstract

This paper presents a sensor fusion method for the Ultra-Tightly Coupled (UTC) Global Positioning System (GPS)/Inertial Navigation System (INS) integrated navigation. The UTC structure, also known as the deep integration, exhibits many advantages, e.g., disturbance and multipath rejection capability, improved tracking capability for dynamic scenarios and weak signals, and reduction of acquisition time. This architecture involves the integration of I (inphase) and Q (quadrature) components from the correlator of a GPS receiver with the INS data. The Particle Filter (PF) exhibits superior performance as compared to an Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) in state estimation for the nonlinear, non-Gaussian system. To handle the problem of heavy-tailed probability distribution, one of the strategies is to incorporate the UKF into the PF as the proposal distribution, leading to the Unscented Particle Filter (UPF). The combination of an adaptive UPF and Fuzzy Logic Adaptive System (FLAS) is adopted for reducing the number of particles with sufficiently good results. The GPS tracking loops may lose lock due to the signals being weak, subjected to excessive dynamics or completely blocked. One of the principal advantages of the UTC structure is that a Doppler frequency derived from the INS is integrated with the tracking loops to improve the receiver tracking capability. The Doppler frequency shift is calculated and fed to the GPS tracking loops for elimination of the effect of stochastic errors caused by the Doppler frequency. In this paper, several nonlinear filtering approaches, including EKF, UKF, UPF and 'FLAS assisted UPF' (FUPF), are adopted for performance comparison for ultra-tight integration of GPS and INS. It is assumed that no outage occurs such that the inertial sensor errors can be properly corrected and accordingly the aiding information is working well. Two examples are provided for performance assessment for the various data fusion methods. The FUPF algorithm with Doppler velocity aiding demonstrates remarkable improvement, especially in the high dynamic environments, in navigation estimation accuracy with reduction of number of particles.

A Least Squares Collocation Method for Accuracy Improvement of Mobile LiDAR Systems

“…In GNSS/IMU integration systems, short-term accuracy depends on the IMU and long-term on the GNSS [24]. Between two neighbouring GNSS epochs, the position and orientation calculated with inertial data, that is gyroscope data and accelerometer data, change both smoothly and consistently.…”

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

“…In traditional U-TC GPS/INS systems the mutual assistance between GPS and INS is strengthened so that the signal tracking ability is much improved [12]. However, the performance of a conventional GNSS/INS U-TC system tends to degrade in challenging environments, such as in case of jamming, or high dynamic maneuvers.…”

“…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].…”

Improving ultrasonic-based seamless navigation for indoor mobile robots utilizing EKF and LS-SVM

“…Many methods have been proposed for GPS/INS integrated navigation, as in, for instance, Han and Wang proposed an integrated GPS/INS navigation system with dual-rate Kalman filter in [6], Fang and Gong proposed a predictive iterated Kalman filter for INS/GPS integration in [7], Jwo et al proposed a novel design for the ultra-tightly coupled GPS/INS navigation system [8]. In the GPS/INS integrated navigation system, the GPS solution is used to update the INS solution, and the INS solution is able to bridge GPS outages [9].…”