GPS/INS

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

Use of inertial sensor data to improve mobile station positioning

Abstract: An implementation of a system and method for using inertial sensor data to improve mobile station positioning is provided. Many mobile devices include an inertial sensor. These inertia measurements typically produce low resolution inertial measurements, which may be used to generate an uncertainty value and/or to select a positioning filter model. In some embodiments, this uncertainty value is used as a process noise input signal into a Kalman filter or similar filter. In some embodiments, one position estimate is selected from multiple navigation position filter models operate concurrently, each computing a separate position estimate. In these embodiments, a position estimate is selected based on either the uncertainty value or based on another characteristic of the inertia measurements.

Robust autonomous GPS time reference for space application

Abstract: A receiver clock bias signal for a spacecraft in motion is is used to obtain a real-time navigation Kalman filter solution of the clock bias and provide a robust autonomous GPS time reference, even when there are less than 4 GPS satellites within the view of the receiver in the spacecraft. The Kalman filter of the spacecraft provides an accurate solution of the clock bias, with less than 4 GPS satellite signals, by means of a system which uses the knowledge of the dynamic motion of the satellite in conjunction with GPS signals for a robust estimation of time. The system provides an accurate GPS time reference by transferring timing information from an atomic reference standard (GPS) to a spacecraft in motion by directly measuring the GPS signal and without depending upon the tracking of multiple GPS satellites or a static receiver. Implementation in current aerospace qualified GPS receivers, e.g., the “GPS TENSOR™”, and known orbital dynamics are used to predict receiver position which aids in the transfer of the GPS time.

GPS/INS integration with fault detection and exclusion in shadowed environments

Abstract: This paper presents a method for GPS/INS operation in shadowed environments such as urban canyons and rural foliage cover. Shadowing causes a combination of multipath and signal attenuation which results in increased uncertainty in the GPS observables and sometimes complete loss of satellite tracking. Environment layout and the line-of-sight vector to the affected satellite determines the degree of shadowing in the range domain. Details are provided for the failure modes and effects in such environments. These results are used in the analysis of a fault detection and exclusion (FDE) algorithm to provide integrity to the GPS observables.

Tightly Coupled Low Cost 3D RISS/GPS Integration Using a Mixture Particle Filter for Vehicular Navigation

Abstract: Satellite navigation systems such as the global positioning system (GPS) are currently the most common technique used for land vehicle positioning. However, in GPS-denied environments, there is an interruption in the positioning information. Low-cost micro-electro mechanical system (MEMS)-based inertial sensors can be integrated with GPS and enhance the performance in denied GPS environments. The traditional technique for this integration problem is Kalman filtering (KF). Due to the inherent errors of low-cost MEMS inertial sensors and their large stochastic drifts, KF, with its linearized models, has limited capabilities in providing accurate positioning. Particle filtering (PF) was recently suggested as a nonlinear filtering technique to accommodate for arbitrary inertial sensor characteristics, motion dynamics and noise distributions. An enhanced version of PF called the Mixture PF is utilized in this study to perform tightly coupled integration of a three dimensional (3D) reduced inertial sensors system (RISS) with GPS. In this work, the RISS consists of one single-axis gyroscope and a two-axis accelerometer used together with the vehicle’s odometer to obtain 3D navigation states. These sensors are then integrated with GPS in a tightly coupled scheme. In loosely-coupled integration, at least four satellites are needed to provide acceptable GPS position and velocity updates for the integration filter. The advantage of the tightly-coupled integration is that it can provide GPS measurement update(s) even when the number of visible satellites is three or lower, thereby improving the operation of the navigation system in environments with partial blockages by providing continuous aiding to the inertial sensors even during limited GPS satellite availability. To effectively exploit the capabilities of PF, advanced modeling for the stochastic drift of the vertically aligned gyroscope is used. In order to benefit from measurement updates for such drift, which are loosely-coupled updates, a hybrid loosely/tightly coupled solution is proposed. This solution is suitable for downtown environments because of the long natural outages or degradation of GPS. The performance of the proposed 3D Navigation solution using Mixture PF for 3D RISS/GPS integration is examined by road test trajectories in a land vehicle and compared to the KF counterpart.

A Reduced-Order Model Integrated GPS/INS for

Abstract: The dominant factor in determining the computation time of the Kalman filter is the dimension n of the model state vector. The number of computations per iteration is on the order of n3. Any reduction in the number of states will benefit directly in terms of increased computation time. In this paper, a high order model in integrated GPS/INS is described first, then a reduced-order model based on the high-order model, is developed. Finally, a faster tracking approach for Kalman filters is discussed.