Topic
GPS/INS
About: GPS/INS is a research topic. Over the lifetime, 3554 publications have been published within this topic receiving 62784 citations.
Papers published on a yearly basis
Papers
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14 May 2012TL;DR: A comparison between the normal operation mode and the backup solution reveals minimal difference between the respective orientation estimates, a position error growth sub-linear with time during GPS outage and a seamless transition back to GPS-based operation.
Abstract: A typical Inertial Navigation System (INS) fuses acceleration and angular rate readings with aiding measurements obtained by GPS and a compass. Here we present a robust state estimation framework based on the Extended Kalman Filter (EKF) applied to low-cost electronics typically installed on-board small unmanned airplanes. It uses airspeed measurements as a backup operation mode replacing GPS updates when temporarily unavailable. We demonstrate the applicability of the proposed approach to real-world scenarios using a challenging dataset recorded on-board a manned glider including long-term circling. A comparison between the normal operation mode and the backup solution reveals minimal difference between the respective orientation estimates, a position error growth sub-linear with time during GPS outage and a seamless transition back to GPS-based operation.
27 citations
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06 Jun 2005TL;DR: This paper investigates the use of LiDAR within an indoor corridor environment (i.e. hallway) to update IMU measurements and shows how this combination is effective in providing accurate state estimates while removing sensor errors due to noise and bias.
Abstract: Autonomous capability requires reliable and robust navigation solutions in multiple environments. GPS has become an effective tool but is not suitable for all environments. Laser scanners are quickly making their presence known in the navigation field and are proven to have a variety of uses. This paper investigates the use of LiDAR within an indoor corridor environment (i.e. hallway) to update IMU measurements. The LiDAR is combined with an IMU in a Kalman filter to produce estimates of vehicle velocity, heading, lateral error, and sensor biases. It is shown how this combination is effective in providing accurate state estimates while removing sensor errors due to noise and bias.
27 citations
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TL;DR: Simulations show that the bias due to aiding doppler offsets resulting in a bias in the tracking loop can be appropriately modelled and removed and could be effectively addressed by appropriate modelling.
Abstract: Tracking dynamics on the GPS signal is still a big challenge to the receiver designer as the operating conditions are becoming more volatile. Optimizing the stand-alone system for dynamics generally degrades the accuracy of measurements. Therefore, an inertial navigation system (INS) is integrated with GPS to address this issue. Doppler derived from INS can be used to aid the carrier tracking loop for improving the performance under dynamic conditions. However, the derived doppler does not truly reflect the GPS signal doppler due to errors in inertial sensors. As the tracking loop bandwidth is reduced significantly in ultra-tightly integrated systems, any offsets in the aiding doppler creates undesired correlations in the tracking loop resulting in sub-optimal performance of the loop. The paper addresses this issue and also provides a mitigating mechanism to reduce the effects of incorrect estimates of the doppler. It is shown that doppler offsets resulting in a bias in the tracking loop can be appropriately modelled and removed. Mathematical algorithms pertaining to this are provided and the results are summarized. Simulations show that the bias due to aiding doppler offsets could be effectively addressed by appropriate modelling.
27 citations
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TL;DR: This paper presents a sensor fusion method based on the combination of cubature Kalman filter and fuzzy logic adaptive system (FLAS) for the integrated navigation systems, such as the GPS/INS/inertial navigation system integration.
Abstract: This paper presents a sensor fusion method based on the combination of cubature Kalman filter (CKF) and fuzzy logic adaptive system (FLAS) for the integrated navigation systems, such as the GPS/INS (Global Positioning System/inertial navigation system) integration. The third-degree spherical-radial cubature rule applied in the CKF has been employed to avoid the numerically instability in the system model. In processing navigation integration, the performance of nonlinear filter based estimation of the position and velocity states may severely degrade caused by modeling errors due to dynamics uncertainties of the vehicle. In order to resolve the shortcoming for selecting the process noise covariance through personal experience or numerical simulation, a scheme called the fuzzy adaptive cubature Kalman filter (FACKF) is presented by introducing the FLAS to adjust the weighting factor of the process noise covariance matrix. The FLAS is incorporated into the CKF framework as a mechanism for timely implementing the tuning of process noise covariance matrix based on the information of degree of divergence (DOD) parameter. The proposed FACKF algorithm shows promising accuracy improvement as compared to the extended Kalman filter (EKF), unscented Kalman filter (UKF), and CKF approaches.
27 citations
27 Sep 2002
TL;DR: This paper presents the real-time flight test results of a GPS/INS navigation system for an Autonomous Unmanned Aerial Vehicle (UAV) developed as a part of the Autonomous Navigation and Sensing Environment Research program between the University of Sydney and BAE Systems.
Abstract: This paper presents the real-time flight test results of a
GPS/INS navigation system for an Autonomous
Unmanned Aerial Vehicle (UAV). The GPS/INS
system was developed as a part of the Autonomous
Navigation and Sensing Environment Research
(ANSER) program between the University of Sydney
and BAE Systems [1]. The system was designed as
loosely coupled integration architecture. The system
was installed and tested on the UAV, Brumby-MK3
developed by the University of Sydney. The flight test
results showed that the GPS/INS navigation system
worked properly in real-time operation and could
provide accurate navigation solutions under high
dynamic and high maneuvering environments.
27 citations