3,066 research outputs found
Communication, Localization and Synchronization of Spacecraft for Swarm Missions
Swarm missions are based on the use of several spacecraft working together to pursue a specific task for a specific mission. To allow these elements to work together, it is necessary for them to be able to communicate with each other and to synchronize themselves within the swarm. Moreover, the mission may likely require knowing the relative or absolute positions of the spacecraft in the swarm.
In order to collect simultaneous measurements allowing computing localization and synchronization in the swarm, a full duplex CDMA communication method is studied by CNES. An Inter Satellite Link (ISL) transmitter prototype is currently under development and first performance evaluation is conducted.
CNES is also working on measurement signal processing. Based on signal exchange between satellites, one can estimate jointly the distance and clock offset between a pair of satellites.
In parallel, CNES is developing a swarm simulator implying both dynamics and functional behavior of each spacecraft in the swarm. First, this simulator will be software only but its architecture will allow integration of hardware equipment in a future version. This simulator will be used for the validation of the services provided by the link at a system level
Opportunistic timing signals for pervasive mobile localization
Mención Internacional en el tÃtulo de doctorThe proliferation of handheld devices and the pressing need of location-based services call for
precise and accurate ubiquitous geographic mobile positioning that can serve a vast set of devices.
Despite the large investments and efforts in academic and industrial communities, a pin-point solution
is however still far from reality. Mobile devices mainly rely on Global Navigation Satellite
System (GNSS) to position themselves. GNSS systems are known to perform poorly in dense urban
areas and indoor environments, where the visibility of GNSS satellites is reduced drastically.
In order to ensure interoperability between the technologies used indoor and outdoor, a pervasive
positioning system should still rely on GNSS, yet complemented with technologies that can
guarantee reliable radio signals in indoor scenarios. The key fact that we exploit is that GNSS signals
are made of data with timing information. We then investigate solutions where opportunistic
timing signals can be extracted out of terrestrial technologies. These signals can then be used as
additional inputs of the multi-lateration problem. Thus, we design and investigate a hybrid system
that combines range measurements from the Global Positioning System (GPS), the world’s
most utilized GNSS system, and terrestrial technologies; the most suitable one to consider in our
investigation is WiFi, thanks to its large deployment in indoor areas. In this context, we first start
investigating standalone WiFi Time-of-flight (ToF)-based localization. Time-of-flight echo techniques
have been recently suggested for ranging mobile devices overWiFi radios. However, these
techniques have yielded only moderate accuracy in indoor environments because WiFi ToF measurements
suffer from extensive device-related noise which makes it challenging to differentiate
between direct path from non-direct path signal components when estimating the ranges. Existing
multipath mitigation techniques tend to fail at identifying the direct path when the device-related
Gaussian noise is in the same order of magnitude, or larger than the multipath noise. In order to
address this challenge, we propose a new method for filtering ranging measurements that is better
suited for the inherent large noise as found in WiFi radios. Our technique combines statistical
learning and robust statistics in a single filter. The filter is lightweight in the sense that it does not
require specialized hardware, the intervention of the user, or cumbersome on-site manual calibration.
This makes the method we propose as the first contribution of the present work particularly
suitable for indoor localization in large-scale deployments using existing legacy WiFi infrastructures.
We evaluate our technique for indoor mobile tracking scenarios in multipath environments,
and, through extensive evaluations across four different testbeds covering areas up to 1000m2, the filter is able to achieve a median ranging error between 1:7 and 2:4 meters.
The next step we envisioned towards preparing theoretical and practical basis for the aforementioned
hybrid positioning system is a deep inspection and investigation of WiFi and GPS ToF
ranges, and initial foundations of single-technology self-localization. Self-localization systems
based on the Time-of-Flight of radio signals are highly susceptible to noise and their performance
therefore heavily rely on the design and parametrization of robust algorithms. We study the noise
sources of GPS and WiFi ToF ranging techniques and compare the performance of different selfpositioning
algorithms at a mobile node using those ranges. Our results show that the localization
error varies greatly depending on the ranging technology, algorithm selection, and appropriate
tuning of the algorithms. We characterize the localization error using real-world measurements
and different parameter settings to provide guidance for the design of robust location estimators
in realistic settings.
These tools and foundations are necessary to tackle the problem of hybrid positioning system
providing high localization capabilities across indoor and outdoor environments. In this context,
the lack of a single positioning system that is able the fulfill the specific requirements of
diverse indoor and outdoor applications settings has led the development of a multitude of localization
technologies. Existing mobile devices such as smartphones therefore commonly rely on
a multi-RAT (Radio Access Technology) architecture to provide pervasive location information
in various environmental contexts as the user is moving. Yet, existing multi-RAT architectures
consider the different localization technologies as monolithic entities and choose the final navigation
position from the RAT that is foreseen to provide the highest accuracy in the particular
context. In contrast, we propose in this work to fuse timing range (Time-of-Flight) measurements
of diverse radio technologies in order to circumvent the limitations of the individual radio access
technologies and improve the overall localization accuracy in different contexts. We introduce
an Extended Kalman filter, modeling the unique noise sources of each ranging technology. As a
rich set of multiple ranges can be available across different RATs, the intelligent selection of the
subset of ranges with accurate timing information is critical to achieve the best positioning accuracy.
We introduce a novel geometrical-statistical approach to best fuse the set of timing ranging
measurements. We also address practical problems of the design space, such as removal of WiFi
chipset and environmental calibration to make the positioning system as autonomous as possible.
Experimental results show that our solution considerably outperforms the use of monolithic
technologies and methods based on classical fault detection and identification typically applied in
standalone GPS technology.
All the contributions and research questions described previously in localization and positioning
related topics suppose full knowledge of the anchors positions. In the last part of this work, we
study the problem of deriving proximity metrics without any prior knowledge of the positions of
the WiFi access points based on WiFi fingerprints, that is, tuples of WiFi Access Points (AP) and
respective received signal strength indicator (RSSI) values. Applications that benefit from proximity
metrics are movement estimation of a single node over time, WiFi fingerprint matching for localization systems and attacks on privacy. Using a large-scale, real-world WiFi fingerprint data
set consisting of 200,000 fingerprints resulting from a large deployment of wearable WiFi sensors,
we show that metrics from related work perform poorly on real-world data. We analyze the
cause for this poor performance, and show that imperfect observations of APs with commodity
WiFi clients in the neighborhood are the root cause. We then propose improved metrics to provide
such proximity estimates, without requiring knowledge of location for the observed AP. We
address the challenge of imperfect observations of APs in the design of these improved metrics.
Our metrics allow to derive a relative distance estimate based on two observed WiFi fingerprints.
We demonstrate that their performance is superior to the related work metrics.This work has been supported by IMDEA Networks InstitutePrograma Oficial de Doctorado en IngenierÃa TelemáticaPresidente: Francisco Barceló Arroyo.- Secretario: Paolo Casari.- Vocal: Marco Fior
Sub-Nanosecond Time of Flight on Commercial Wi-Fi Cards
Time-of-flight, i.e., the time incurred by a signal to travel from
transmitter to receiver, is perhaps the most intuitive way to measure distances
using wireless signals. It is used in major positioning systems such as GPS,
RADAR, and SONAR. However, attempts at using time-of-flight for indoor
localization have failed to deliver acceptable accuracy due to fundamental
limitations in measuring time on Wi-Fi and other RF consumer technologies.
While the research community has developed alternatives for RF-based indoor
localization that do not require time-of-flight, those approaches have their
own limitations that hamper their use in practice. In particular, many existing
approaches need receivers with large antenna arrays while commercial Wi-Fi
nodes have two or three antennas. Other systems require fingerprinting the
environment to create signal maps. More fundamentally, none of these methods
support indoor positioning between a pair of Wi-Fi devices
without~third~party~support.
In this paper, we present a set of algorithms that measure the time-of-flight
to sub-nanosecond accuracy on commercial Wi-Fi cards. We implement these
algorithms and demonstrate a system that achieves accurate device-to-device
localization, i.e. enables a pair of Wi-Fi devices to locate each other without
any support from the infrastructure, not even the location of the access
points.Comment: 14 page
Forest Variable Estimation Using a High Altitude Single Photon Lidar System
As part of the digitalization of the forest planning process, 3D remote sensing data is an important data source. However, the demand for more detailed information with high temporal resolution and yet still being cost efficient is a challenging combination for the systems used today. A new lidar technology based on single photon counting has the possibility to meet these needs. The aim of this paper is to evaluate the new single photon lidar sensor Leica SPL100 for area-based forest variable estimations. In this study, it was found that data from the new system, operated from 3800 m above ground level, could be used for raster cell estimates with similar or slightly better accuracy than a linear system, with similar point density, operated from 400 m above ground level. The new single photon counting lidar sensor shows great potential to meet the need for efficient collection of detailed information, due to high altitude, flight speed and pulse repetition rate. Further research is needed to improve the method for extraction of information and to investigate the limitations and drawbacks with the technology. The authors emphasize solar noise filtering in forest environments and the effect of different atmospheric conditions, as interesting subjects for further research
Measures de vent 3D avec le lidar Doppler coherent Live à bord d'un avion
International audienceA three-dimensional (3D) wind profiling Lidar, based on the latest high power 1.5 µm fiber laser development at Onera, has been successfully flown on-board a SAFIRE (Service des Avions Français Instrumentés pour la Recherche en Environnement) ATR42 aircraft. The Lidar called LIVE (LIdar VEnt) is designed to measure wind profiles from the aircraft down to ground level, with a horizontal resolution of 3 km, a vertical resolution of 100 m and a designed accuracy on each three wind vector components better than 0.5 m.s −1. To achieve the required performance, LIVE Lidar emits 410 µJ laser pulses repeating at 14 KHz with a duration of 700 ns and uses a conical scanner of 30 • total opening angle and a full scan time of 17 s.Un lidar vent 3D, basé sur le dernier développement de laser à fibre de 1,5 µm à haute puissance de l’ONERA a été testé avec succès à bord d’un avion SAFIRE ATR42. Le lidar appelé LIVE est conçu pour mesurer les profils de vent de l’avion jusqu'au sol, avec une résolution horizontale de 3 km, une résolution verticale de 100 m et une précision calculée supérieure à 0,5 m / s pour chaque composante du vecteur du vent
Ultrasonic inline inspection of a cement-based drinking water pipeline
The integrity of the drinking water infrastructure deteriorates with time. Monitoring the condition of the drinking water mains can enhance the remaining operational lifetime assessment of the network. In this research a method to translate ultrasonic signals to degradation levels from an inline inspection in a cement-based drinking water pipeline is proposed. The data was obtained from an inspection performed in a Dutch drinking main section. The data is processed in two major steps. Firstly, the parameters that provide the condition of the cement are extracted. Secondly, images of the degradation within the pipes of the inspected trajectory were generated. The main contributions in this paper are (i) the estimation of relative degradation levels of a cement-based pipeline based on the ultrasonic pulse-echo technique and (ii) the upscaling of the processing method in an automated manner for visualization of the degraded condition. Lastly, a sensitivity study of the parameters relevant to the determination of the degraded depth has been performed. The speed of sound in cement is the most relevant parameter to consider. Estimating absolute degradation levels needs further study.</p
Feature-based underwater localization using an imaging sonar
The ability of an AUV to locate itself in an environment as well as to detect relevant environmental features is of key importance for navigation success. Sonars are one the most common sensing devices for underwater localization and mapping, being used to detect and identify underwater structural features. This study explores the processing and analysis of acoustic images, through the data acquired by a mechanical scanning imaging sonar, in order to extract relevant environmental features that enable location estimation. For this purpose, the performances of different state-of-the art feature extraction algorithms were evaluated. Furthermore, an improvement to the feature matching step is proposed, in order to adapt this procedure to the characteristics of acoustic images. The extracted features are then used to feed a location estimator composed of a Simultaneous Localization and Mapping algorithm implementing an Extended Kalman Filter. Several tests were performed in a structured environment and the results of the feature extraction process and localization are presented
- …