7,545 research outputs found
A Survey of Positioning Systems Using Visible LED Lights
© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.As Global Positioning System (GPS) cannot provide satisfying performance in indoor environments, indoor positioning technology, which utilizes indoor wireless signals instead of GPS signals, has grown rapidly in recent years. Meanwhile, visible light communication (VLC) using light devices such as light emitting diodes (LEDs) has been deemed to be a promising candidate in the heterogeneous wireless networks that may collaborate with radio frequencies (RF) wireless networks. In particular, light-fidelity has a great potential for deployment in future indoor environments because of its high throughput and security advantages. This paper provides a comprehensive study of a novel positioning technology based on visible white LED lights, which has attracted much attention from both academia and industry. The essential characteristics and principles of this system are deeply discussed, and relevant positioning algorithms and designs are classified and elaborated. This paper undertakes a thorough investigation into current LED-based indoor positioning systems and compares their performance through many aspects, such as test environment, accuracy, and cost. It presents indoor hybrid positioning systems among VLC and other systems (e.g., inertial sensors and RF systems). We also review and classify outdoor VLC positioning applications for the first time. Finally, this paper surveys major advances as well as open issues, challenges, and future research directions in VLC positioning systems.Peer reviewe
Massive MIMO-based Localization and Mapping Exploiting Phase Information of Multipath Components
In this paper, we present a robust multipath-based localization and mapping
framework that exploits the phases of specular multipath components (MPCs)
using a massive multiple-input multiple-output (MIMO) array at the base
station. Utilizing the phase information related to the propagation distances
of the MPCs enables the possibility of localization with extraordinary accuracy
even with limited bandwidth. The specular MPC parameters along with the
parameters of the noise and the dense multipath component (DMC) are tracked
using an extended Kalman filter (EKF), which enables to preserve the
distance-related phase changes of the MPC complex amplitudes. The DMC comprises
all non-resolvable MPCs, which occur due to finite measurement aperture. The
estimation of the DMC parameters enhances the estimation quality of the
specular MPCs and therefore also the quality of localization and mapping. The
estimated MPC propagation distances are subsequently used as input to a
distance-based localization and mapping algorithm. This algorithm does not need
prior knowledge about the surrounding environment and base station position.
The performance is demonstrated with real radio-channel measurements using an
antenna array with 128 ports at the base station side and a standard cellular
signal bandwidth of 40 MHz. The results show that high accuracy localization is
possible even with such a low bandwidth.Comment: 14 pages (two columns), 13 figures. This work has been submitted to
the IEEE Transaction on Wireless Communications for possible publication.
Copyright may be transferred without notice, after which this version may no
longer be accessibl
Position and Orientation Estimation through Millimeter Wave MIMO in 5G Systems
Millimeter wave signals and large antenna arrays are considered enabling
technologies for future 5G networks. While their benefits for achieving
high-data rate communications are well-known, their potential advantages for
accurate positioning are largely undiscovered. We derive the Cram\'{e}r-Rao
bound (CRB) on position and rotation angle estimation uncertainty from
millimeter wave signals from a single transmitter, in the presence of
scatterers. We also present a novel two-stage algorithm for position and
rotation angle estimation that attains the CRB for average to high
signal-to-noise ratio. The algorithm is based on multiple measurement vectors
matching pursuit for coarse estimation, followed by a refinement stage based on
the space-alternating generalized expectation maximization algorithm. We find
that accurate position and rotation angle estimation is possible using signals
from a single transmitter, in either line-of- sight, non-line-of-sight, or
obstructed-line-of-sight conditions.Comment: The manuscript has been revised, and increased from 27 to 31 pages.
Also, Fig.2, Fig. 10 and Table I are adde
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Pedestrian localisation for indoor environments
Ubiquitous computing systems aim to assist us as we go about our daily lives, whilst at the same time fading into the background so that we do not notice their presence. To do this they need to be able to sense their surroundings and infer context about the state of the world. Location has proven to be an important source of contextual information for such systems. If a device can determine its own location then it can infer its surroundings and adapt accordingly.
Of particular interest for many ubiquitous computing systems is the ability to track people in indoor environments. This interest has led to the development of many indoor location systems based on a range of technologies including infra-red light, ultrasound and radio. Unfortunately existing systems that achieve the kind of sub-metre accuracies desired by many location-aware applications require large amounts of infrastructure to be installed into the environment.
This thesis investigates an alternative approach to indoor pedestrian tracking that uses on-body inertial sensors rather than relying on fixed infrastructure. It is demonstrated that general purpose inertial navigation algorithms are unsuitable for pedestrian tracking due to the rapid accumulation of errors in the tracked position. In practice it is necessary to frequently correct such algorithms using additional measurements or constraints. An extended Kalman filter
is developed for this purpose and is applied to track pedestrians using foot-mounted inertial sensors. By detecting when the foot is stationary and applying zero velocity corrections a pedestrian’s relative movements can be tracked far more accurately than is possible using uncorrected inertial navigation.
Having developed an effective means of calculating a pedestrian’s relative movements, a localisation filter is developed that combines relative movement measurements with environmental constraints derived from a map of the environment. By enforcing constraints such as impassable walls and floors the filter is able to narrow down the absolute position of a pedestrian as they move through an indoor environment. Once the user’s position has been uniquely determined the same filter is demonstrated to track the user’s absolute position to sub-metre accuracy.
The localisation filter in its simplest form is computationally expensive. Furthermore symmetry exhibited by the environment may delay or prevent the filter from determining the user’s position. The final part of this thesis describes the concept of assisted localisation, in which additional measurements are used to solve both of these problems. The use of sparsely deployed WiFi access points is discussed in detail.
The thesis concludes that inertial sensors can be used to track pedestrians in indoor environments. Such an approach is suited to cases in which it is impossible or impractical to install large amounts of fixed infrastructure into the environment in advance
A Survey on Joint Object Detection and Pose Estimation using Monocular Vision
In this survey we present a complete landscape of joint object detection and
pose estimation methods that use monocular vision. Descriptions of traditional
approaches that involve descriptors or models and various estimation methods
have been provided. These descriptors or models include chordiograms,
shape-aware deformable parts model, bag of boundaries, distance transform
templates, natural 3D markers and facet features whereas the estimation methods
include iterative clustering estimation, probabilistic networks and iterative
genetic matching. Hybrid approaches that use handcrafted feature extraction
followed by estimation by deep learning methods have been outlined. We have
investigated and compared, wherever possible, pure deep learning based
approaches (single stage and multi stage) for this problem. Comprehensive
details of the various accuracy measures and metrics have been illustrated. For
the purpose of giving a clear overview, the characteristics of relevant
datasets are discussed. The trends that prevailed from the infancy of this
problem until now have also been highlighted.Comment: Accepted at the International Joint Conference on Computer Vision and
Pattern Recognition (CCVPR) 201
AoA-aware Probabilistic Indoor Location Fingerprinting using Channel State Information
With expeditious development of wireless communications, location
fingerprinting (LF) has nurtured considerable indoor location based services
(ILBSs) in the field of Internet of Things (IoT). For most pattern-matching
based LF solutions, previous works either appeal to the simple received signal
strength (RSS), which suffers from dramatic performance degradation due to
sophisticated environmental dynamics, or rely on the fine-grained physical
layer channel state information (CSI), whose intricate structure leads to an
increased computational complexity. Meanwhile, the harsh indoor environment can
also breed similar radio signatures among certain predefined reference points
(RPs), which may be randomly distributed in the area of interest, thus mightily
tampering the location mapping accuracy. To work out these dilemmas, during the
offline site survey, we first adopt autoregressive (AR) modeling entropy of CSI
amplitude as location fingerprint, which shares the structural simplicity of
RSS while reserving the most location-specific statistical channel information.
Moreover, an additional angle of arrival (AoA) fingerprint can be accurately
retrieved from CSI phase through an enhanced subspace based algorithm, which
serves to further eliminate the error-prone RP candidates. In the online phase,
by exploiting both CSI amplitude and phase information, a novel bivariate
kernel regression scheme is proposed to precisely infer the target's location.
Results from extensive indoor experiments validate the superior localization
performance of our proposed system over previous approaches
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