532 research outputs found
Multisensor navigation systems: a remedy for GNSS vulnerabilities?
Space-based positioning, navigation, and timing (PNT) technologies, such as the global navigation satellite systems (GNSS) provide position, velocity, and timing information to an unlimited number of users around the world. In recent years, PNT information has become increasingly critical to the security, safety, and prosperity of the World's population, and is now widely recognized as an essential element of the global information infrastructure. Due to its vulnerabilities and line-of-sight requirements, GNSS alone is unable to provide PNT with the required levels of integrity, accuracy, continuity, and reliability. A multisensor navigation approach offers an effective augmentation in GNSS-challenged environments that holds a promise of delivering robust and resilient PNT. Traditionally, sensors such as inertial measurement units (IMUs), barometers, magnetometers, odometers, and digital compasses, have been used. However, recent trends have largely focused on image-based, terrain-based and collaborative navigation to recover the user location. This paper offers a review of the technological advances that have taken place in PNT over the last two decades, and discusses various hybridizations of multisensory systems, building upon the fundamental GNSS/IMU integration. The most important conclusion of this study is that in order to meet the challenging goals of delivering continuous, accurate and robust PNT to the ever-growing numbers of users, the hybridization of a suite of different PNT solutions is required
Grid-based Hybrid 3DMA GNSS and Terrestrial Positioning
The paper discusses the increasing use of hybridized sensor information for
GNSS-based localization and navigation, including the use of 3D map-aided GNSS
positioning and terrestrial systems based on different geometric measurement
principles. However, both GNSS and terrestrial systems are subject to negative
impacts from the propagation environment, which can violate the assumptions of
conventionally applied parametric state estimators. Furthermore, dynamic
parametric state estimation does not account for multi-modalities within the
state space leading to an information loss within the prediction step. In
addition, the synchronization of non-deterministic multi-rate measurement
systems needs to be accounted.
In order to address these challenges, the paper proposes the use of a
non-parametric filtering method, specifically a 3DMA multi-epoch Grid Filter,
for the tight integration of GNSS and terrestrial signals. Specifically, the
fusion of GNSS, Ultra-wide Band (UWB) and vehicle motion data is introduced
based on a discrete state representation. Algorithmic challenges, including the
use of different measurement models and time synchronization, are addressed. In
order to evaluate the proposed method, real-world tests were conducted on an
urban automotive testbed in both static and dynamic scenarios.
We empirically show that we achieve sub-meter accuracy in the static scenario
by averaging a positioning error of m, whereas in the dynamic scenario
the average positioning error amounts to m.
The paper provides a proof-of-concept of the introduced method and shows the
feasibility of the inclusion of terrestrial signals in a 3DMA positioning
framework in order to further enhance localization in GNSS-degraded
environments
Dirichlet Process Mixtures for Density Estimation in Dynamic Nonlinear Modeling: Application to GPS Positioning in Urban Canyons
International audienceIn global positioning systems (GPS), classical localization algorithms assume, when the signal is received from the satellite in line-of-sight (LOS) environment, that the pseudorange error distribution is Gaussian. Such assumption is in some way very restrictive since a random error in the pseudorange measure with an unknown distribution form is always induced in constrained environments especially in urban canyons due to multipath/masking effects. In order to ensure high accuracy positioning, a good estimation of the observation error in these cases is required. To address this, an attractive flexible Bayesian nonparametric noise model based on Dirichlet process mixtures (DPM) is introduced. Since the considered positioning problem involves elements of non-Gaussianity and nonlinearity and besides, it should be processed on-line, the suitability of the proposed modeling scheme in a joint state/parameter estimation problem is handled by an efficient Rao-Blackwellized particle filter (RBPF). Our approach is illustrated on a data analysis task dealing with joint estimation of vehicles positions and pseudorange errors in a global navigation satellite system (GNSS)-based localization context where the GPS information may be inaccurate because of hard reception conditions
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