Topic
GPS/INS
About: GPS/INS is a research topic. Over the lifetime, 3554 publications have been published within this topic receiving 62784 citations.
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Patent•
20 Sep 2000
TL;DR: In this article, an antenna pattern is applied to the signals received at the elements of the GPS phased array antenna to provide a composite signal to each of the processing channels of the digital GPS receiver, optimized for the particular GPS satellite being tracked by a corresponding channel.
Abstract: A digital GPS receiver includes electronics to detect the presence of multipath GPS signals, determine the direction from which they are received at a multi-element GPS phased array antenna, adaptively generate an antenna pattern to provide gain in the direction of the desired GPS satellite signal, and to apply nulls in the direction of the detected GPS multipath signals. This adaptively-generated antenna pattern is applied to the signals received at the elements of the GPS phased array antenna to provide a composite signal to each of the processing channels of the digital GPS receiver, optimized for the particular GPS satellite being tracked by a corresponding channel. The undesirable multipath GPS signals are thereby excised from the inputs to the processing channels of the digital GPS receiver, and the desired direct signal is reinforced, thus enabling tracking loops within the digital GPS receiver to make highly accurate observations of the code and carrier phase using conventional signal processing techniques.
27 citations
TL;DR: In this article, an adaptive filter combined with the fading factor is applied in IMU/GPS integrated navigation system. But the results prove that the adaptive filter is valid and reliable.
Abstract: The IMU(inertial measurement unit) error equations in the earth fixed coordinates are introduced firstly. A fading Kalman filtering is simply introduced and its shortcomings are analyzed, then an adaptive filtering is applied in IMU/GPS integrated navigation system, in which the adaptive factor is replaced by the fading factor. A practical example is given. The results prove that the adaptive filter combined with the fading factor is valid and reliable when applied in IMU/GPS integrated navigation system.
27 citations
TL;DR: The paper presents an algorithm for estimating a pedestrian location in an urban environment that is based on the particle filter and uses different data sources: a GPS receiver, inertial sensors, probability maps and a stereo camera.
Abstract: The paper presents an algorithm for estimating a pedestrian location in an urban environment. The algorithm is based on the particle filter and uses different data sources: a GPS receiver, inertial sensors, probability maps and a stereo camera. Inertial sensors are used to estimate a relative displacement of a pedestrian. A gyroscope estimates a change in the heading direction. An accelerometer is used to count a pedestrian's steps and their lengths. The so-called probability maps help to limit GPS inaccuracy by imposing constraints on pedestrian kinematics, e.g., it is assumed that a pedestrian cannot cross buildings, fences etc. This limits position inaccuracy to ca. 10 m. Incorporation of depth estimates derived from a stereo camera that are compared to the 3D model of an environment has enabled further reduction of positioning errors. As a result, for 90% of the time, the algorithm is able to estimate a pedestrian location with an error smaller than 2 m, compared to an error of 6.5 m for a navigation based solely on GPS.
27 citations
26 Dec 2006
TL;DR: The IMM-EKF solution presented in this paper allows the exploitation of highly dynamic models just when required, avoiding the impoverishment of the solution due to unrealistic noise considerations during straight or mild trajectories.
Abstract: Actual solutions for the road vehicle navigation problem point to the combination of GPS, odometry and inertial sensors. To combine the information coming from these sensors, most of actual researchers rely on the implementation of variations of the Kalman filter (KF) and the extended Kalman filter (EKFF) for non-linear systems. Despite the fact that, in these filters, the definition of the proper vehicle model is of extreme importance, there is not a unique common filter suitable for all the usual situations in which a road vehicle is involved. The diversity of possible maneuvers and the need of realistic noise considerations adjusted to each driving situation encourage the application of IMM (interactive multi-model) techniques in the road navigation. Traditionally applied to the aerial sector, IMM based methods run different models at the same time, selecting that one which better represents the system behavior anytime. For road vehicles, the IMM-EKF solution presented in this paper allows the exploitation of highly dynamic models just when required, avoiding the impoverishment of the solution due to unrealistic noise considerations during straight or mild trajectories. Selected results presented in this paper confirm the improvements obtained by using the IMM-EKF developed, as compared with the single model solution.
27 citations
TL;DR: The proposed method was developed, tested and validated through road test experiments involving both low-end tactical grade and low cost MEMS-based inertial systems and demonstrates that in most cases the position accuracy during GPS outages using FOS de-noised data is superior to the positions accuracy using wavelet de- noising.
Abstract: In both military and civilian applications, the inertial navigation system (INS) and the global positioning system (GPS) are two complementary technologies that can be integrated to provide reliable positioning and navigation information for land vehicles. The accuracy enhancement of INS sensors and the integration of INS with GPS are the subjects of widespread research. Wavelet de-noising of INS sensors has had limited success in removing the long-term (low-frequency) inertial sensor errors. The primary objective of this research is to develop a novel inertial sensor accuracy enhancement technique that can remove both short-term and long-term error components from inertial sensor measurements prior to INS mechanization and INS/GPS integration. A high resolution spectral analysis technique called the fast orthogonal search (FOS) algorithm is used to accurately model the low frequency range of the spectrum, which includes the vehicle motion dynamics and inertial sensor errors. FOS models the spectral components with the most energy first and uses an adaptive threshold to stop adding frequency terms when fitting a term does not reduce the mean squared error more than fitting white noise. The proposed method was developed, tested and validated through road test experiments involving both low-end tactical grade and low cost MEMS-based inertial systems. The results demonstrate that in most cases the position accuracy during GPS outages using FOS de-noised data is superior to the position accuracy using wavelet de-noising.
27 citations