GPS/INS

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

GPS-Aided Gyroscope-Free Inertial Navigation Systems

Abstract: A gyroscope-free inertial navigation system uses only accelerometers to compute navigation trajectories It is a low-cost navigation system, but its output error diverges at a rate that is an order faster than that of a conventional gyroscope-based system So integration with an external reference system, such as the Global Positioning System, is necessary for long-term navigation applications In this pa-per, an integrated GPS and gyroscope-free INS system is designed to achieve stable long-term navigation The linear and nonlinear error models of a gyroscope-free INS are derived and are used as Kalman filter equations to estimate the errors in the gyroscope-free INS data The effects of gyroscope-free inertial measurement unit errors are also analyzed By using computer simulations, the performance of the integrated GPS and gyroscope-free INS system is verified

Bounded Constrained Filtering for GPS/INS Integration

Abstract: This paper considers estimation problems where inequality constraints are imposed on the outputs of linear systems and can be modeled by nonlinear functions. In this case, censoring functions can be designed to constrain measurements for use by filters and smoothers. It is established that the filter and smoother output estimates are unbiased, provided that the underlying probability density functions are even and the censoring functions are odd. The Bounded Real Lemma is employed to ensure that the output estimates satisfy a performance criterion. A global positioning system (GPS) and inertial navigation system (INS) integration application is discussed in which a developed solution exhibits improved performance during GPS outages when a priori information is used to constrain the altitude and velocity measurements.

Study on adaptive GPS/INS integrated navigation system

Abstract: A GPS/INS integrated navigation system based on the theory of multi-sensor data fusion is presented in this paper. Error models for the inertial measurement unit are generated and included in the extended Kalman filter for INS. An improved decentralized Kalman filter is developed to eliminate obvious error of GPS data and reduce the load of calculation. An adaptive federal Kalman filter is used to fuse the data from GPS and INS to provide smoothed and continuous positioning data against the presence of GPS blockages or communication dropouts and the unbounded INS errors growing with time. The results demonstrate that the performance of the adaptive GPS/INS integrated navigation system is better than that of using only GPS of INS.

Integrated gps/ins navigation system design for autonomous spacecraft rendezvous

Abstract: The goal of the NASA Space Launch Initiative (SLI) program is to advance the technologies for the next generation reusable launch vehicle (RLV). The SLI program has identified automated rendezvous and docking as an area requiring further research and development. Currently, the Space Shuttle uses a partially manual system for rendezvous, but a fully automated system could be safer and more reliable. Previous studies have shown that it is feasible to use the Global Positioning System (GPS) for spacecraft navigation during rendezvous with the International Space Station (ISS). However, these studies have not accounted for the effects of GPS signal blockage and multipath in the vicinity of the ISS, which make a GPS-only navigation system less accurate and reliable. One possible solution is to combine GPS with an inertial navigation system (INS). The integration of GPS and INS can be achieved using a Kalman filter. GPS/INS systems have been used in aircraft for many years and have also been used in launch vehicles. However, the performance of GPS/INS systems in orbit and during spacecraft rendezvous has not been characterized. The primary objective of this research is to evaluate the ability of an integrated GPS/INS to provide accurate navigation solutions during a rendezvous scenario where the effects of ISS signal blockage, multipath and delta-v maneuvers degrade GPS-only navigation. In order to accomplish this, GPS-only and GPS/INS Kalman filters have been developed for both absolute and relative navigation, as well as a new statistical multipath model for spacecraft operating near the ISS. Several factors that affect relative navigation performance were studied, including: filter tuning, GPS constellation geometry, rendezvous approach direction, and inertial sensor performance. The results showed that each of these factors has a large impact on relative navigation performance. Finally, it has been demonstrated that a GPS/INS system based on medium accuracy aircraft avionics-grade inertial sensors does not provide adequate relative navigation performance for rendezvous with the ISS unless accelerometer thresholding is used. However, the use of state-of-the-art inertial navigation sensors provides relative position accuracy which is adequate for rendezvous with ISS if an additional rendezvous sensor is included.

FPGA-Based Real-Time Embedded System for RISS/GPS Integrated Navigation

Abstract: Navigation algorithms integrating measurements from multi-sensor systems overcome the problems that arise from using GPS navigation systems in standalone mode. Algorithms which integrate the data from 2D low-cost reduced inertial sensor system (RISS), consisting of a gyroscope and an odometer or wheel encoders, along with a GPS receiver via a Kalman filter has proved to be worthy in providing a consistent and more reliable navigation solution compared to standalone GPS receivers. It has been also shown to be beneficial, especially in GPS-denied environments such as urban canyons and tunnels. The main objective of this paper is to narrow the idea-to-implementation gap that follows the algorithm development by realizing a low-cost real-time embedded navigation system capable of computing the data-fused positioning solution. The role of the developed system is to synchronize the measurements from the three sensors, relative to the pulse per second signal generated from the GPS, after which the navigation algorithm is applied to the synchronized measurements to compute the navigation solution in real-time. Employing a customizable soft-core processor on an FPGA in the kernel of the navigation system, provided the flexibility for communicating with the various sensors and the computation capability required by the Kalman filter integration algorithm.