971 research outputs found
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
On signal strength-based distance estimation using UWB technology
Ultra-wideband (UWB) technology has become very popular for indoor
positioning and distance estimation (DE) systems due to its decimeter-level
accuracy achieved when using time-of-flight-based techniques. Techniques for DE
relying on signal strength (DESS) received less attention. As a consequence,
existing benchmarks consist of simple channel characterizations rather than
methods aiming to increase accuracy. Further development in DESS may enable
lower-cost transceivers to applications that can afford lower accuracies than
those based on time-of-flight. Moreover, it is a fundamental building block
used by a recently proposed approach that can enable security against
cyberattacks on DE which could not be avoided using only time-of-flight-based
techniques. In this paper, we evaluate the suitability of several
machine-learning models trained in different real-world environments to
increase UWB-based DESS accuracy. Additionally, aiming for implementation in
commercial off-the-shelf (COTS) transceivers, we propose and evaluate an
approach to resolve ambiguities comprising DESS in these devices. Our results
show that the proposed DE approaches have sub-decimeter accuracy when testing
the models in the same environment and positions in which they have been
trained, and achieved an average MAE of 24 cm when tested in a different
environment. 3 datasets obtained from our experiments are made publicly
available
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