321 research outputs found
Performance Evaluation of Mobile U-Navigation based on GPS/WLAN Hybridization
This paper present our mobile u-navigation system. This approach utilizes
hybridization of wireless local area network and Global Positioning System
internal sensor which to receive signal strength from access point and the same
time retrieve Global Navigation System Satellite signal. This positioning
information will be switched based on type of environment in order to ensure
the ubiquity of positioning system. Finally we present our results to
illustrate the performance of the localization system for an indoor/ outdoor
environment set-up.Comment: Journal of Convergence Information Technology(JCIT
Spatial Diversity in Signal Strength Based WLAN Location Determination Systems
Literature indicates that spatial diversity can be utilized to compensate channel uncertainties such as multipath fading. Therefore, in this paper, spatial diversity is exploited for locating stationary and mobile objects in the indoor environment. First, space diversity technique is introduced for small scale motion and temporal variation compensation of received signal strength and it is demonstrated analytically that it enhances location accuracy. Small scale motion refers to movements of the transmitter and/or the receiver of the order of sub-wavelengths while temporal effects refer to environmental variations with time. A novel metric is introduced for selection combining in order to improve location accuracy through the addition of spatial diversity upon two available location determination schemes. The results are evaluated experimentally against single antenna system for reception by using low cost wireless RF devices such as motes. Alternatively, the impact of the number of location determination devices in a probabilistic WLAN network based on pre-profiling of signal strength is analyzed and it is demonstrated analytically that location accuracy improves with the number of receivers used. Spatial diversity in terms of the antenna spacing of 2lambda is evaluated and shown to provide a reduction in location determination error between 30 and 40% when compared to a single antenna system
Diversity techniques for signal-strength based indoor location determination
Diversity techniques have been found in the literature to be suitable for compensating channel uncertainties such as multipath fading. In this thesis, we exploit spatial and frequency diversity techniques for improving accuracy in locating stationary and mobile objects in the indoor environment --Abstract, page iv
A Robust Zero-Calibration RF-based Localization System for Realistic Environments
Due to the noisy indoor radio propagation channel, Radio Frequency (RF)-based
location determination systems usually require a tedious calibration phase to
construct an RF fingerprint of the area of interest. This fingerprint varies
with the used mobile device, changes of the transmit power of smart access
points (APs), and dynamic changes in the environment; requiring re-calibration
of the area of interest; which reduces the technology ease of use. In this
paper, we present IncVoronoi: a novel system that can provide zero-calibration
accurate RF-based indoor localization that works in realistic environments. The
basic idea is that the relative relation between the received signal strength
from two APs at a certain location reflects the relative distance from this
location to the respective APs. Building on this, IncVoronoi incrementally
reduces the user ambiguity region based on refining the Voronoi tessellation of
the area of interest. IncVoronoi also includes a number of modules to
efficiently run in realtime as well as to handle practical deployment issues
including the noisy wireless environment, obstacles in the environment,
heterogeneous devices hardware, and smart APs. We have deployed IncVoronoi on
different Android phones using the iBeacons technology in a university campus.
Evaluation of IncVoronoi with a side-by-side comparison with traditional
fingerprinting techniques shows that it can achieve a consistent median
accuracy of 2.8m under different scenarios with a low beacon density of one
beacon every 44m2. Compared to fingerprinting techniques, whose accuracy
degrades by at least 156%, this accuracy comes with no training overhead and is
robust to the different user devices, different transmit powers, and over
temporal changes in the environment. This highlights the promise of IncVoronoi
as a next generation indoor localization system.Comment: 9 pages, 13 figures, published in SECON 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
A ranging method with IEEE 802.11 data frames for indoor localization
IEEE 802.11 networks constitute a suitable infrastructure for accurate indoor positioning. However, existing approaches based on fingerprinting present drawbacks that make them not suitable for most of applications. This paper presents an innovative TOA-based ranging technique over IEEE 802.11 networks intended to be the essential step of an indoors location system. This approach is based on round trip time measurements using standard IEEE 802.11 link layer frames and a statistical post-processing to mitigate the noise of the measurements. A prototype has been implemented in order to assess the validity and evaluate the performance of the proposed technique. First results show ranging accuracies of less than one meter of error in LOS situations
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