26,068 research outputs found
Statistical Learning Theory for Location Fingerprinting in Wireless LANs
In this paper, techniques and algorithms developed in the framework of statistical learning theory are analyzed and applied to the problem of determining the location of a wireless device by measuring the signal strengths from a set of access points (location fingerprinting). Statistical Learning Theory provides a rich theoretical basis for the development of models starting from a set of examples. Signal strength measurement is part of the normal operating mode of wireless equipment, in particular Wi-Fi, so that no custom hardware is required. The proposed techniques, based on the Support Vector Machine paradigm, have been implemented and compared, on the same data set, with other approaches considered in the literature. Tests performed in a real-world environment show that results are comparable, with the advantage of a low algorithmic complexity in the normal operating phase. Moreover, the algorithm is particularly suitable for classification, where it outperforms the other techniques
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
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
Fixed Rank Kriging for Cellular Coverage Analysis
Coverage planning and optimization is one of the most crucial tasks for a
radio network operator. Efficient coverage optimization requires accurate
coverage estimation. This estimation relies on geo-located field measurements
which are gathered today during highly expensive drive tests (DT); and will be
reported in the near future by users' mobile devices thanks to the 3GPP
Minimizing Drive Tests (MDT) feature~\cite{3GPPproposal}. This feature consists
in an automatic reporting of the radio measurements associated with the
geographic location of the user's mobile device. Such a solution is still
costly in terms of battery consumption and signaling overhead. Therefore,
predicting the coverage on a location where no measurements are available
remains a key and challenging task. This paper describes a powerful tool that
gives an accurate coverage prediction on the whole area of interest: it builds
a coverage map by spatially interpolating geo-located measurements using the
Kriging technique. The paper focuses on the reduction of the computational
complexity of the Kriging algorithm by applying Fixed Rank Kriging (FRK). The
performance evaluation of the FRK algorithm both on simulated measurements and
real field measurements shows a good trade-off between prediction efficiency
and computational complexity. In order to go a step further towards the
operational application of the proposed algorithm, a multicellular use-case is
studied. Simulation results show a good performance in terms of coverage
prediction and detection of the best serving cell
Statistical Modeling and Estimation of Censored Pathloss Data
Pathloss is typically modeled using a log-distance power law with a
large-scale fading term that is log-normal. However, the received signal is
affected by the dynamic range and noise floor of the measurement system used to
sound the channel, which can cause measurement samples to be truncated or
censored. If the information about the censored samples are not included in the
estimation method, as in ordinary least squares estimation, it can result in
biased estimation of both the pathloss exponent and the large scale fading.
This can be solved by applying a Tobit maximum-likelihood estimator, which
provides consistent estimates for the pathloss parameters. This letter provides
information about the Tobit maximum-likelihood estimator and its asymptotic
variance under certain conditions.Comment: 4 pages, 3 figures. Published in IEEE Wireless Communication Letter
Observability of Path Loss Parameters in WLAN-Based Simultaneous Localization and Mapping
Indoor positioning by means of received signal strengths has been gathering much interest since the massive presence of wireless local area networks (WLANs) in buildings. Theoretical approaches rely on the perfect knowledge of the APs' positions and propagation conditions; since this is unrealistic in real world, we estimate such knowledge as well as the building map from data by applying Simultaneous Localization and Mapping (SLAM).
In this paper we address the joint estimation of the path loss parameters, namely the transmitted power and the path loss exponent, this latter being usually approximated in the literature by the free space value. We provide examples that show the relevance of estimating both parameters and analyze observability issues from the point of view of estimation theory. The integration of the parameter estimation in a WLAN based SLAM algorithm - WiSLAM - has been carried out and the results are discussed
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