GPS/INS

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

A Low-Cost, High-Precision Vehicle Navigation System for Deep Urban Multipath Environment Using TDCP Measurements.

Abstract: In this study, we developed a low-cost, high-precision vehicle navigation system for deep urban multipath environments using time-differenced carrier phase (TDCP) measurements. Although many studies are being conducted to navigate autonomous vehicles using the global positioning system (GPS), it is difficult to obtain accurate navigation solutions due to multipath errors in urban environments. Low-cost GPS receivers that determine the solution based on pseudorange measurements are vulnerable to multipath errors. We used carrier phase measurements that are more robust for multipath errors. Without correction information from reference stations, the limited information of a low-cost, single-frequency receiver makes it difficult to quickly and accurately determine integer ambiguity of carrier phase measurements. We used TDCP measurements to eliminate the need to determine integer ambiguity that is time-invariant and we combined TDCP-based GPS with an inertial navigation system to overcome deep urban multipath environments. Furthermore, we considered a cycle slip algorithm for its accuracy and a multi-constellation navigation system for its availability. The results of dynamic field tests in a deep urban area indicated that it could achieve horizontal accuracy of at the submeter level.

Development of a GPS/INS/MAG navigation system and waypoint navigator for a VTOL UAV

Abstract: Unmanned aerial vehicles (UAV) can be used for versatile surveillance and reconnaissance missions. If a UAV is capable of flying automatically on a predefined path the range of possible applications is widened significantly. This paper addresses the development of the integrated GPS/INS/MAG navigation system and a waypoint navigator for a small vertical take-off and landing (VTOL) unmanned four-rotor helicopter with a take-off weight below 1 kg. The core of the navigation system consists of low cost inertial sensors which are continuously aided with GPS, magnetometer compass, and a barometric height information. Due to the fact, that the yaw angle becomes unobservable during hovering flight, the integration with a magnetic compass is mandatory. This integration must be robust with respect to errors caused by the terrestrial magnetic field deviation and interferences from surrounding electronic devices as well as ferrite metals. The described integration concept with a Kalman filter overcomes the problem that erroneous magnetic measurements yield to an attitude error in the roll and pitch axis. The algorithm provides long-term stable navigation information even during GPS outages which is mandatory for the flight control of the UAV. In the second part of the paper the guidance algorithms are discussed in detail. These algorithms allow the UAV to operate in a semi-autonomous mode position hold as well an complete autonomous waypoint mode. In the position hold mode the helicopter maintains its position regardless of wind disturbances which ease the pilot job during hold-and-stare missions. The autonomous waypoint navigator enable the flight outside the range of vision and beyond the range of the radio link. Flight test results of the implemented modes of operation are shown.

Differential global positioning system using coarse GPS data for a fast time to a precise first fix

Abstract: A differential global positioning system (DGPS) using coarse data for the locations-in-space of GPS satellites for ranging to the GPS satellites and then determining a precise differentially corrected GPS location of a GPS user receiver. The coarse GPS data can be GPS almanac data or non-current GPS ephemeris data as long as the same data is used by both a GPS reference receiver and the GPS user receiver.

Deep Learning-Enabled Fusion to Bridge GPS Outages for INS/GPS Integrated Navigation

Abstract: The low cost inertial navigation system (INS) suffers from bias and measurement noise, which would result in poor navigation accuracy during global positioning system (GPS) outages. Aiming to bridge GPS outages duration and enhance the navigation performance, a deep learning network architecture named GPS/INS neural network (GI-NN) is proposed in this paper to assist the INS. The GI-NN combines a convolutional neural network and a gated recurrent unit neural network to extract spatial features from inertial measurement unit (IMU) signals and track their temporal characteristics. The relationship among the attitude, specific force, angular rate and the GPS position increment is modelled, while the current and previous IMU data are used to estimate the dynamics of the vehicle by GI-NN. Numerical simulations, real field tests and public data tests are performed to evaluate the effectiveness of the proposed algorithm. Compared with the traditional machine learning algorithms, the results illustrate that the proposed method can provide more accurate and reliable navigation solutions in GPS denied environments.

GPS Based Relative Navigation

Abstract: The use of Global Positioning System (GPS) measurements provides the primary technique for determining the relative position of cooperative, formation-flying satellites in low Earth orbit Similar to terrestrial applications, the relative navigation benefits from a high level of common error cancellation Furthermore, the integer nature of double-difference carrier phase ambiguities can be exploited in carrier phase differential GPS (CDGPS) Both aspects enable a substantially higher relative accuracy than can be achieved in single-spacecraft navigation Following an overview of spaceborne GPS receivers, the dynamical and measurement models for relative navigation using single- or dual-frequency measurements are presented along with a discussion of estimation schemes for real-time and offline applications Actual flight results from the TanDEM-X and PRISMA missions are presented to demonstrate the feasibility of mm-level post-facto baseline determination and cm-level real-time navigation using CDGPS