6,638 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
Selective AP-sequence Based Indoor Localization without Site Survey
In this paper, we propose an indoor localization system employing ordered
sequence of access points (APs) based on received signal strength (RSS). Unlike
existing indoor localization systems, our approach does not require any
time-consuming and laborious site survey phase to characterize the radio
signals in the environment. To be precise, we construct the fingerprint map by
cutting the layouts of the interested area into regions with only the knowledge
of positions of APs. This can be done offline within a second and has a
potential for practical use. The localization is then achieved by matching the
ordered AP-sequence to the ones in the fingerprint map. Different from
traditional fingerprinting that employing all APs information, we use only
selected APs to perform localization, due to the fact that, without site
survey, the possibility in obtaining the correct AP sequence is lower if it
involves more APs. Experimental results show that, the proposed system achieves
localization accuracy < 5m with an accumulative density function (CDF) of 50%
to 60% depending on the density of APs. Furthermore, we observe that, using all
APs for localization might not achieve the best localization accuracy, e.g. in
our case, 4 APs out of total 7 APs achieves the best performance. In practice,
the number of APs used to perform localization should be a design parameter
based on the placement of APs.Comment: VTC2016-Spring, 15-18 May 2016, Nanjing, Chin
EWOk: towards efficient multidimensional compression of indoor positioning datasets
Indoor positioning performed directly at the end-user device ensures reliability in case the network connection fails but is limited by the size of the RSS radio map necessary to match the measured array to the deviceâs location. Reducing the size of the RSS database enables faster processing, and saves storage space and radio resources necessary for the database transfer, thus cutting implementation and operation costs, and increasing the quality of service. In this work, we propose EWOk, an Element-Wise cOmpression using k-means, which reduces the size of the individual radio measurements within the fingerprinting radio map while sustaining or boosting the datasetâs positioning capabilities. We show that the 7-bit representation of measurements is sufficient in positioning scenarios, and reducing the data size further using EWOk results in higher compression and faster data transfer and processing. To eliminate the inherent uncertainty of k-means we propose a data-dependent, non-random initiation scheme to ensure stability and limit variance. We further combine EWOk with principal component analysis to show its applicability in combination with other methods, and to demonstrate the efficiency of the resulting multidimensional compression. We evaluate EWOk on 25 RSS fingerprinting datasets and show that it positively impacts compression efficiency, and positioning performance.This work was supported by the European Unionâs Horizon 2020 Research and Innovation programme under the Marie Sklodowska Curie grant agreements No. 813278 (A-WEAR: A network for dynamic wearable applications
with privacy constraints, http://www.a-wear.eu/) and No. 101023072 (ORIENTATE: Low-cost Reliable Indoor Positioning in Smart Factories, http://orientate.dsi.uminho.pt) and Academy of Finland (grants #319994,
#323244)
Intelligent Indoor Parking
Nowadays positioning based navigation is an integrated part of our everydayâs routine. Hence, it is hard to succeed without a GPS based navigation system in a bigger city today. However, indoor positioning and navigation are still in their infancy, although these services would be desirable in many areas. One obvious application domain is vehicle navigation in a parking garage. The use of an indoor vehicle navigation system is convenient for the drivers, decreases the unnecessary circling in the garage and reduces air pollution. In this paper, we introduce our iParking indoor positioning and navigation system which has been under development. Our system monitors the occupancy of the parking lots, and with the aid of a Wi-Fi based background wireless infrastructure tracks the position of the vehicle entering the parking garage and navigates the driver to an appropriate free parking lot. Lot selection is handled at the entry point of the garage based on simple preferences, eg., the closest disabled parking space. The navigation interface is the driverâs smartphone. Currently, we have been implementing a prototype of our iParking system in a parking garage of a shopping mall for demonstration purposes
New Cluster Selection and Fine-grained Search for k-Means Clustering and Wi-Fi Fingerprinting
Ponencia presentada en 2020 International Conference on Localization and GNSS (ICL-GNSS), 02-04 June 2020, Tampere, FinlandWi-Fi fingerprinting is a popular technique for Indoor Positioning Systems (IPSs) thanks to its low complexity and the ubiquity of WLAN infrastructures. However, this technique may present scalability issues when the reference dataset (radio map) is very large. To reduce the computational costs, k-Means Clustering has been successfully applied in the past. However, it is a general-purpose algorithm for unsupervised classification. This paper introduces three variants that apply heuristics based on radio propagation knowledge in the coarse and fine-grained searches. Due to the heterogeneity either in the IPS side (including radio map generation) and in the network infrastructure, we used an evaluation framework composed of 16 datasets. In terms of general positioning accuracy and computational costs, the best proposed k-means variant provided better general positioning accuracy and a significantly better computational cost âaround 40% lowerâ than the original k-means
Catalog Matching with Astrometric Correction and its Application to the Hubble Legacy Archive
Object cross-identification in multiple observations is often complicated by
the uncertainties in their astrometric calibration. Due to the lack of standard
reference objects, an image with a small field of view can have significantly
larger errors in its absolute positioning than the relative precision of the
detected sources within. We present a new general solution for the relative
astrometry that quickly refines the World Coordinate System of overlapping
fields. The efficiency is obtained through the use of infinitesimal 3-D
rotations on the celestial sphere, which do not involve trigonometric
functions. They also enable an analytic solution to an important step in making
the astrometric corrections. In cases with many overlapping images, the correct
identification of detections that match together across different images is
difficult to determine. We describe a new greedy Bayesian approach for
selecting the best object matches across a large number of overlapping images.
The methods are developed and demonstrated on the Hubble Legacy Archive, one of
the most challenging data sets today. We describe a novel catalog compiled from
many Hubble Space Telescope observations, where the detections are combined
into a searchable collection of matches that link the individual detections.
The matches provide descriptions of astronomical objects involving multiple
wavelengths and epochs. High relative positional accuracy of objects is
achieved across the Hubble images, often sub-pixel precision in the order of
just a few milli-arcseconds. The result is a reliable set of high-quality
associations that are publicly available online.Comment: 9 pages, 9 figures, accepted for publication in the Astrophysical
Journa
Parking Assistance System for Indoor Environment
In the last decade, indoor location based applications, such as touristic guide in museums or shopping guidance in supermarkets, have been developed rapidly requiring suitable and accurate indoor positioning. However, location sensing in indoor environments is a challenging task and an intensively researched topic. Fortunately, wireless technologies can help us derive location information. In this paper, we propose and introduce our positioning and navigation system for indoor parking garage environment, called iParking, which has been under development. The iParking system collects real-time parking lot occupancy data, and tracks and navigates vehicles entering the parking garage to a preselected, e.g., the closest to the favorite shop, free parking lot. The driverâs smartphone is used as the navigation interface. The system is built on a background Wi-Fi infrastructure making the deployment and maintenance economical. Currently, we have been implementing a prototype of our iParking system in a parking garage of a shopping mall for demonstration purposes
Wi-Fi Fingerprinting for Indoor Positioning
Wireless Fidelity (Wi-Fi) Fingerprinting is a remarkable approach developed by modern science to detect the userââŹâ˘s location efficiently. Today, the Global Positioning System (GPS) is used to keep track of our current location for outdoor positioning. In GPS technology, satellite signals cannot reach indoor environments as they are shielded from obstructions so that indoor environments with a lack of Line of Sight (LoS) do not provide enough satellite signal accuracy. Since indoor environments are very difficult to track, thus, a wide variety of techniques for dealing with them have been suggested. The best way to offer an indoor positioning service with the current technology is Wi-Fi since the most commercial infrastructure is well equipped with Wi-Fi routers. For indoor positioning systems (IPS), Wi-Fi fingerprinting approaches are being extremely popular. In this paper, all the approaches for Wi-Fi fingerprinting have been reviewed for indoor position localization. Related to Wi-Fi fingerprinting, most of the algorithms have been interpreted and the previous works of other researchers have been critically analyzed in this paper to get a clear view of the Wi-Fi fingerprinting process
Wi-Fi Fingerprinting for Indoor Positioning
Wireless Fidelity (Wi-Fi) Fingerprinting is a remarkable approach developed by modern science to detect the userââŹâ˘s location efficiently. Today, the Global Positioning System (GPS) is used to keep track of our current location for outdoor positioning. In GPS technology, satellite signals cannot reach indoor environments as they are shielded from obstructions so that indoor environments with a lack of Line of Sight (LoS) do not provide enough satellite signal accuracy. Since indoor environments are very difficult to track, thus, a wide variety of techniques for dealing with them have been suggested. The best way to offer an indoor positioning service with the current technology is Wi-Fi since the most commercial infrastructure is well equipped with Wi-Fi routers. For indoor positioning systems (IPS), Wi-Fi fingerprinting approaches are being extremely popular. In this paper, all the approaches for Wi-Fi fingerprinting have been reviewed for indoor position localization. Related to Wi-Fi fingerprinting, most of the algorithms have been interpreted and the previous works of other researchers have been critically analyzed in this paper to get a clear view of the Wi-Fi fingerprinting process
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