1,523 research outputs found
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
Location-Enabled IoT (LE-IoT): A Survey of Positioning Techniques, Error Sources, and Mitigation
The Internet of Things (IoT) has started to empower the future of many
industrial and mass-market applications. Localization techniques are becoming
key to add location context to IoT data without human perception and
intervention. Meanwhile, the newly-emerged Low-Power Wide-Area Network (LPWAN)
technologies have advantages such as long-range, low power consumption, low
cost, massive connections, and the capability for communication in both indoor
and outdoor areas. These features make LPWAN signals strong candidates for
mass-market localization applications. However, there are various error sources
that have limited localization performance by using such IoT signals. This
paper reviews the IoT localization system through the following sequence: IoT
localization system review -- localization data sources -- localization
algorithms -- localization error sources and mitigation -- localization
performance evaluation. Compared to the related surveys, this paper has a more
comprehensive and state-of-the-art review on IoT localization methods, an
original review on IoT localization error sources and mitigation, an original
review on IoT localization performance evaluation, and a more comprehensive
review of IoT localization applications, opportunities, and challenges. Thus,
this survey provides comprehensive guidance for peers who are interested in
enabling localization ability in the existing IoT systems, using IoT systems
for localization, or integrating IoT signals with the existing localization
sensors
Inter-Node Distance Estimation from Multipath Delay Differences of Channels to Observer Nodes
We study the estimation of distance d between two wireless nodes by means of
their wideband channels to a third node, called observer. The motivating
principle is that the channel impulse responses are similar for small d and
drift apart when d increases. Following this idea we propose specific distance
estimators based on the differences of path delays of the extractable multipath
components. In particular, we derive such estimators for rich multipath
environments and various important cases: with and without clock
synchronization as well as errors on the extracted path delays (e.g. due to
limited bandwidth). The estimators readily support (and benefit from) the
presence of multiple observers. We present an error analysis and, using ray
tracing in an exemplary indoor environment, show that the estimators perform
well in realistic conditions. We describe possible localization applications of
the proposed scheme and highlight its major advantages: it requires neither
precise synchronization nor line-of-sight connection. This could make wireless
user tracking feasible in dynamic indoor settings.Comment: To appear at IEEE ICC 2019. This work has been submitted to the IEEE
for possible publication. Copyright may be transferred without notice, after
which this version may no longer be accessibl
Amplitude Modeling of Specular Multipath Components for Robust Indoor Localization
Ultra-Wide Bandwidth (UWB) and mm-wave radio systems can resolve specular multipath components (SMCs) from estimated channel impulse response measurements. A geometric model can describe the delays, angles-of-arrival, and angles-of-departure of these SMCs, allowing for a prediction of these channel features. For the modeling of the amplitudes of the SMCs, a data-driven approach has been proposed recently, using Gaussian Process Regression (GPR) to map and predict the SMC amplitudes. In this paper, the applicability of the proposed multipath-resolved, GPR-based channel model is analyzed by studying features of the propagation channel from a set of channel measurements. The features analyzed include the energy capture of the modeled SMCs, the number of resolvable SMCs, and the ranging information that could be extracted from the SMCs. The second contribution of the paper concerns the potential applicability of the channel model for a multipath-resolved, single-anchor positioning system. The predicted channel knowledge is used to evaluate the measurement likelihood function at candidate positions throughout the environment. It is shown that the environmental awareness created by the multipath-resolved, GPR-based channel model yields higher robustness against position estimation outliers
Soft information for localization-of-things
Location awareness is vital for emerging Internetof-
Things applications and opens a new era for Localizationof-
Things. This paper first reviews the classical localization
techniques based on single-value metrics, such as range and
angle estimates, and on fixed measurement models, such as
Gaussian distributions with mean equal to the true value of the
metric. Then, it presents a new localization approach based
on soft information (SI) extracted from intra- and inter-node
measurements, as well as from contextual data. In particular,
efficient techniques for learning and fusing different kinds of SI
are described. Case studies are presented for two scenarios in
which sensing measurements are based on: 1) noisy features
and non-line-of-sight detector outputs and 2) IEEE 802.15.4a
standard. The results show that SI-based localization is highly
efficient, can significantly outperform classical techniques, and
provides robustness to harsh propagation conditions.RYC-2016-1938
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