GPS/INS

About: GPS/INS is a research topic. Over the lifetime, 3554 publications have been published within this topic receiving 62784 citations.

Highly integrated gps, galileo and inertial navigation system

Abstract: The navigation system described here utilizes GPS and Galileo satellite signals combined with Inertial Navigation Systems (INS), where a Coupled Antenna (CAN) provides both GNSS and Inertial Measurement Unit (IMU) data to a Highly Integrated GNSS-Inertial (Hi-Gi) receiver. Such receiver makes use of a high fidelity relation between GNSS unprocessed Correlator Output (COUT) I and Q data and the user trajectory, and inertial sensor data, which in turn are combined within a Kalman Filter (KF). The KF determines the navigation solution that is also used to provide feedback to the receiver demodulation signal processing stage, thus eliminating the need of dedicated structures such as Delayed Locked Loops (DLL) and Phase Locked Loops (PLL), allowing a significant improvement in navigation performance. The improvement allows this system to provide high quality measurements and operate in circumstances where usual techniques are not usable; for example during satellite signal interruption due to obstruction, or in very high dynamics or even in attenuated signal environments, due to, for example, the canopy of trees. The KF also makes use of particular Galileo signal characteristics, lock detectors and Coupled Antenna that allow the system to operate in such environments.

Tracking drone orientation with multiple GPS receivers

Abstract: Inertial sensors continuously track the 3D orientation of a flying drone, serving as the bedrock for maneuvers and stabilization. However, even the best inertial measurement units (IMU) are prone to various types of correlated failures. We consider using multiple GPS receivers on the drone as a fail-safe mechanism for IMU failures. The core challenge is in accurately computing the relative locations between each receiver pair, and translating these measurements into the drone's 3D orientation. Achieving IMU-like orientation requires the relative GPS distances to be accurate to a few centimeters -- a difficult task given that GPS today is only accurate to around 1-4 meters. Moreover, GPS-based orientation needs to be precise even under sharp drone maneuvers, GPS signal blockage, and sudden bouts of missing data. This paper designs SafetyNet, an off-the-shelf GPS-only system that addresses these challenges through a series of techniques, culminating in a novel particle filter framework running over multi-GNSS systems (GPS, GLONASS, and SBAS). Results from 11 sessions of 5-7 minute flights report median orientation accuracies of 2° even under overcast weather conditions. Of course, these improvements arise from an increase in cost due to the multiple GPS receivers, however, when safety is of interest, we believe that tradeoff is worthwhile.

Inertial gps navigation system using injected alignment data for the inertial system

Abstract: An inertial ('INS')/GPS receiver uses injected alignment data to determine the alignment of the INS sub-system when the receiver is in motion during start-up. The alignment data is determined from parameterized surface information, measured GPS velocity, and a known or predetermined angular relationship between the vehicle on which the receiver is mounted and an inertial measurement reference, or body, frame associated with the accelerometers and gyroscopes of the inertial measuring unit ('IMU'). The parameterized surface information, which provides a constraint, may be the orientation of the surface over which the vehicle that houses the receiver is moving. The receiver uses the initial GPS position to determine the location of the vehicle on the parameterized surface, and thus, the known surface orientation. The receiver then determines the roll, pitch and heading of the vehicle on the surface using the associated GPS velocity vector. Thereafter, the receiver uses the calculated roll, pitch and heading of the vehicle and the known or predetermined angular relationship between the vehicle and the IMU body frame to determine a rotation matrix that relates the IMU body frame to a computation or referenced frame used by the receiver.

Loosely-coupled integration of global navigation satellite system and inertial navigation system: speed scale-factor and heading bias calibration

Abstract: Techniques for loosely coupling a Global Navigation Satellite System (“GNSS”) and an Inertial Navigation System (“INS”) integration are disclosed herein. A system includes a GNSS receiver, an INS, and an integration filter coupled to the GNSS receiver and the INS. The GNSS receiver is configured to provide GNSS navigation information comprising GNSS receiver position and/or velocity estimates. The INS is configured to provide INS navigation information based on an inertial sensor output. The integration filter is configured to provide blended position information comprising a blended position estimate and/or a blended velocity estimate by combining the GNSS navigation information and the INS navigation information, and to estimate and compensate at least one of a speed scale-factor and a heading bias of the INS navigation information.