636 research outputs found
Map-Aware Models for Indoor Wireless Localization Systems: An Experimental Study
The accuracy of indoor wireless localization systems can be substantially
enhanced by map-awareness, i.e., by the knowledge of the map of the environment
in which localization signals are acquired. In fact, this knowledge can be
exploited to cancel out, at least to some extent, the signal degradation due to
propagation through physical obstructions, i.e., to the so called
non-line-of-sight bias. This result can be achieved by developing novel
localization techniques that rely on proper map-aware statistical modelling of
the measurements they process. In this manuscript a unified statistical model
for the measurements acquired in map-aware localization systems based on
time-of-arrival and received signal strength techniques is developed and its
experimental validation is illustrated. Finally, the accuracy of the proposed
map-aware model is assessed and compared with that offered by its map-unaware
counterparts. Our numerical results show that, when the quality of acquired
measurements is poor, map-aware modelling can enhance localization accuracy by
up to 110% in certain scenarios.Comment: 13 pages, 11 figures, 1 table. IEEE Transactions on Wireless
Communications, 201
Multi Detector Fusion of Dynamic TOA Estimation using Kalman Filter
In this paper, we propose fusion of dynamic TOA (time of arrival) from
multiple non-coherent detectors like energy detectors operating at sub-Nyquist
rate through Kalman filtering. We also show that by using multiple of these
energy detectors, we can achieve the performance of a digital matched filter
implementation in the AWGN (additive white Gaussian noise) setting. We derive
analytical expression for number of energy detectors needed to achieve the
matched filter performance. We demonstrate in simulation the validity of our
analytical approach. Results indicate that number of energy detectors needed
will be high at low SNRs and converge to a constant number as the SNR
increases. We also study the performance of the strategy proposed using IEEE
802.15.4a CM1 channel model and show in simulation that two sub-Nyquist
detectors are sufficient to match the performance of digital matched filter
D-SLATS: Distributed Simultaneous Localization and Time Synchronization
Through the last decade, we have witnessed a surge of Internet of Things
(IoT) devices, and with that a greater need to choreograph their actions across
both time and space. Although these two problems, namely time synchronization
and localization, share many aspects in common, they are traditionally treated
separately or combined on centralized approaches that results in an ineffcient
use of resources, or in solutions that are not scalable in terms of the number
of IoT devices. Therefore, we propose D-SLATS, a framework comprised of three
different and independent algorithms to jointly solve time synchronization and
localization problems in a distributed fashion. The First two algorithms are
based mainly on the distributed Extended Kalman Filter (EKF) whereas the third
one uses optimization techniques. No fusion center is required, and the devices
only communicate with their neighbors. The proposed methods are evaluated on
custom Ultra-Wideband communication Testbed and a quadrotor, representing a
network of both static and mobile nodes. Our algorithms achieve up to three
microseconds time synchronization accuracy and 30 cm localization error
Accurate Positioning in Ultra-Wideband Systems
Cataloged from PDF version of article.Accurate positioning systems can be realized via ultra-wideband signals due to their high time resolution. In this article, position estimation is studied for UWB systems. After a brief introduction to UWB signals and their positioning applications,
two-step positioning systems are investigated from a UWB perspective. It is observed that time-based positioning is well suited for UWB systems. Then time-based UWB ranging is studied in detail, and the main challenges, theoretical limits, and range estimation algorithms are presented. Performance of some practical time-based ranging algorithms is investigated and compared against the maximum likelihood estimator and the theoretical limits. The trade-off between complexity and accuracy is .observe
Indoor wireless communications and applications
Chapter 3 addresses challenges in radio link and system design in indoor scenarios. Given the fact that most human activities take place in indoor environments, the need for supporting ubiquitous indoor data connectivity and location/tracking service becomes even more important than in the previous decades. Specific technical challenges addressed in this section are(i), modelling complex indoor radio channels for effective antenna deployment, (ii), potential of millimeter-wave (mm-wave) radios for supporting higher data rates, and (iii), feasible indoor localisation and tracking techniques, which are summarised in three dedicated sections of this chapter
Loosely Coupled Odometry, UWB Ranging, and Cooperative Spatial Detection for Relative Monte-Carlo Multi-Robot Localization
As mobile robots become more ubiquitous, their deployments grow across use
cases where GNSS positioning is either unavailable or unreliable. This has led
to increased interest in multi-modal relative localization methods.
Complementing onboard odometry, ranging allows for relative state estimation,
with ultra-wideband (UWB) ranging having gained widespread recognition due to
its low cost and centimeter-level out-of-box accuracy. Infrastructure-free
localization methods allow for more dynamic, ad-hoc, and flexible deployments,
yet they have received less attention from the research community. In this
work, we propose a cooperative relative multi-robot localization where we
leverage inter-robot ranging and simultaneous spatial detections of objects in
the environment. To achieve this, we equip robots with a single UWB transceiver
and a stereo camera. We propose a novel Monte-Carlo approach to estimate
relative states by either employing only UWB ranges or dynamically integrating
simultaneous spatial detections from the stereo cameras. We also address the
challenges for UWB ranging error mitigation, especially in non-line-of-sight,
with a study on different LSTM networks to estimate the ranging error. The
proposed approach has multiple benefits. First, we show that a single range is
enough to estimate the accurate relative states of two robots when fusing
odometry measurements. Second, our experiments also demonstrate that our
approach surpasses traditional methods such as multilateration in terms of
accuracy. Third, to increase accuracy even further, we allow for the
integration of cooperative spatial detections. Finally, we show how ROS 2 and
Zenoh can be integrated to build a scalable wireless communication solution for
multi-robot systems. The experimental validation includes real-time deployment
and autonomous navigation based on the relative positioning method
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