328 research outputs found
K-Means Fingerprint Clustering for Low-Complexity Floor Estimation in Indoor Mobile Localization
Indoor localization in multi-floor buildings is an important research
problem. Finding the correct floor, in a fast and efficient manner, in a
shopping mall or an unknown university building can save the users' search time
and can enable a myriad of Location Based Services in the future. One of the
most widely spread techniques for floor estimation in multi-floor buildings is
the fingerprinting-based localization using Received Signal Strength (RSS)
measurements coming from indoor networks, such as WLAN and BLE. The clear
advantage of RSS-based floor estimation is its ease of implementation on a
multitude of mobile devices at the Application Programming Interface (API)
level, because RSS values are directly accessible through API interface.
However, the downside of a fingerprinting approach, especially for large-scale
floor estimation and positioning solutions, is their need to store and transmit
a huge amount of fingerprinting data. The problem becomes more severe when the
localization is intended to be done on mobile devices which have limited
memory, power, and computational resources. An alternative floor estimation
method, which has lower complexity and is faster than the fingerprinting is the
Weighted Centroid Localization (WCL) method. The trade-off is however paid in
terms of a lower accuracy than the one obtained with traditional fingerprinting
with Nearest Neighbour (NN) estimates. In this paper a novel K-means-based
method for floor estimation via fingerprint clustering of WiFi and various
other positioning sensor outputs is introduced. Our method achieves a floor
estimation accuracy close to the one with NN fingerprinting, while
significantly improves the complexity and the speed of the floor detection
algorithm. The decrease in the database size is achieved through storing and
transmitting only the cluster heads (CH's) and their corresponding floor
labels.Comment: Accepted to IEEE Globecom 2015, Workshop on Localization and
Tracking: Indoors, Outdoors and Emerging Network
Advanced real-time indoor tracking based on the Viterbi algorithm and semantic data
A real-time indoor tracking system based on the Viterbi algorithm is developed. This Viterbi principle is used in combination with semantic data to improve the accuracy, that is, the environment of the object that is being tracked and a motion model. The starting point is a fingerprinting technique for which an advanced network planner is used to automatically construct the radio map, avoiding a time consuming measurement campaign. The developed algorithm was verified with simulations and with experiments in a building-wide testbed for sensor experiments, where a median accuracy below 2 m was obtained. Compared to a reference algorithm without Viterbi or semantic data, the results indicated a significant improvement: the mean accuracy and standard deviation improved by, respectively, 26.1% and 65.3%. Thereafter a sensitivity analysis was conducted to estimate the influence of node density, grid size, memory usage, and semantic data on the performance
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
Multiple simultaneous Wi-Fi measurements in fingerprinting indoor positioning
The accuracy of fingerprinting-based positioning methods accuracy is limited by the fluctuations in the radio signal intensity mainly due to reflections, refractions, and multipath interference, among other factors. We consider that the fluctuations (often modelled as a Gaussian process for simplification purposes) can be minimized by exploiting the richness of multiple signals collected simultaneously through independent network interfaces. This paper introduces an analysis of Wi-Fi signals' statistics using simultaneous measurements which shows that RSSI values obtained from independent devices are not highly correlated. The low correlation between Wi-Fi interfaces might be exploited to improve the positioning accuracy. The validation of the proposed fingerprinting approach in a real scenario shows that the mean and maximum error in positioning can be reduced by more than 40% when five Wi-Fi interfaces are simultaneously used for fingerprinting.This work has been supported by COMPETE: POCI-01- 0145-FEDER-007043 and FCT—Fundação para a Ciência e Tecnologia within the scope of project UID/CEC/00319/2013, by the Portugal Incentive System for Research and Technological Development in the scope of the projects in co- promotion no 002814/2015 (iFACTORY 2015-2018), and by the José Castillejo mobility grant (CAS16/00072).info:eu-repo/semantics/publishedVersio
A Soft Range Limited K-Nearest Neighbours Algorithm for Indoor Localization Enhancement
This paper proposes a soft range limited K nearest neighbours (SRL-KNN)
localization fingerprinting algorithm. The conventional KNN determines the
neighbours of a user by calculating and ranking the fingerprint distance
measured at the unknown user location and the reference locations in the
database. Different from that method, SRL-KNN scales the fingerprint distance
by a range factor related to the physical distance between the user's previous
position and the reference location in the database to reduce the spatial
ambiguity in localization. Although utilizing the prior locations, SRL-KNN does
not require knowledge of the exact moving speed and direction of the user.
Moreover, to take into account of the temporal fluctuations of the received
signal strength indicator (RSSI), RSSI histogram is incorporated into the
distance calculation. Actual on-site experiments demonstrate that the new
algorithm achieves an average localization error of m with of the
errors under m, which outperforms conventional KNN algorithms by
under the same test environment.Comment: Received signal strength indicator (RSSI), WiFi indoor localization,
K-nearest neighbor (KNN), fingerprint-based localizatio
Semi-Sequential Probabilistic Model For Indoor Localization Enhancement
This paper proposes a semi-sequential probabilistic model (SSP) that applies
an additional short term memory to enhance the performance of the probabilistic
indoor localization. The conventional probabilistic methods normally treat the
locations in the database indiscriminately. In contrast, SSP leverages the
information of the previous position to determine the probable location since
the user's speed in an indoor environment is bounded and locations near the
previous one have higher probability than the other locations. Although the SSP
utilizes the previous location information, it does not require the exact
moving speed and direction of the user. On-site experiments using the received
signal strength indicator (RSSI) and channel state information (CSI)
fingerprints for localization demonstrate that SSP reduces the maximum error
and boosts the performance of existing probabilistic approaches by 25% - 30%
Distributed and adaptive location identification system for mobile devices
Indoor location identification and navigation need to be as simple, seamless,
and ubiquitous as its outdoor GPS-based counterpart is. It would be of great
convenience to the mobile user to be able to continue navigating seamlessly as
he or she moves from a GPS-clear outdoor environment into an indoor environment
or a GPS-obstructed outdoor environment such as a tunnel or forest. Existing
infrastructure-based indoor localization systems lack such capability, on top
of potentially facing several critical technical challenges such as increased
cost of installation, centralization, lack of reliability, poor localization
accuracy, poor adaptation to the dynamics of the surrounding environment,
latency, system-level and computational complexities, repetitive
labor-intensive parameter tuning, and user privacy. To this end, this paper
presents a novel mechanism with the potential to overcome most (if not all) of
the abovementioned challenges. The proposed mechanism is simple, distributed,
adaptive, collaborative, and cost-effective. Based on the proposed algorithm, a
mobile blind device can potentially utilize, as GPS-like reference nodes,
either in-range location-aware compatible mobile devices or preinstalled
low-cost infrastructure-less location-aware beacon nodes. The proposed approach
is model-based and calibration-free that uses the received signal strength to
periodically and collaboratively measure and update the radio frequency
characteristics of the operating environment to estimate the distances to the
reference nodes. Trilateration is then used by the blind device to identify its
own location, similar to that used in the GPS-based system. Simulation and
empirical testing ascertained that the proposed approach can potentially be the
core of future indoor and GPS-obstructed environments
- …