1,019 research outputs found
RF Localization in Indoor Environment
In this paper indoor localization system based on the RF power measurements of the Received Signal Strength (RSS) in WLAN environment is presented. Today, the most viable solution for localization is the RSS fingerprinting based approach, where in order to establish a relationship between RSS values and location, different machine learning approaches are used. The advantage of this approach based on WLAN technology is that it does not need new infrastructure (it reuses already and widely deployed equipment), and the RSS measurement is part of the normal operating mode of wireless equipment. We derive the Cramer-Rao Lower Bound (CRLB) of localization accuracy for RSS measurements. In analysis of the bound we give insight in localization performance and deployment issues of a localization system, which could help designing an efficient localization system. To compare different machine learning approaches we developed a localization system based on an artificial neural network, k-nearest neighbors, probabilistic method based on the Gaussian kernel and the histogram method. We tested the developed system in real world WLAN indoor environment, where realistic RSS measurements were collected. Experimental comparison of the results has been investigated and average location estimation error of around 2 meters was obtained
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
Space-partitioning with cascade-connected ANN structures for positioning in mobile communication systems
The world around us is getting more connected with each day passing by – new portable
devices employing wireless connections to various networks wherever one might be. Locationaware
computing has become an important bit of telecommunication services and industry. For
this reason, the research efforts on new and improved localisation algorithms are constantly
being performed. Thus far, the satellite positioning systems have achieved highest popularity
and penetration regarding the global position estimation. In spite the numerous investigations
aimed at enabling these systems to equally procure the position in both indoor and outdoor
environments, this is still a task to be completed.
This research work presented herein aimed at improving the state-of-the-art positioning
techniques through the use of two highly popular mobile communication systems: WLAN and
public land mobile networks. These systems already have widely deployed network structures
(coverage) and a vast number of (inexpensive) mobile clients, so using them for additional,
positioning purposes is rational and logical.
First, the positioning in WLAN systems was analysed and elaborated. The indoor test-bed,
used for verifying the models’ performances, covered almost 10,000m2 area. It has been chosen
carefully so that the positioning could be thoroughly explored. The measurement campaigns
performed therein covered the whole of test-bed environment and gave insight into location
dependent parameters available in WLAN networks. Further analysis of the data lead to
developing of positioning models based on ANNs.
The best single ANN model obtained 9.26m average distance error and 7.75m median distance
error. The novel positioning model structure, consisting of cascade-connected ANNs, improved
those results to 8.14m and 4.57m, respectively. To adequately compare the proposed
techniques with other, well-known research techniques, the environment positioning error
parameter was introduced. This parameter enables to take the size of the test environment into
account when comparing the accuracy of the indoor positioning techniques.
Concerning the PLMN positioning, in-depth analysis of available system parameters and
signalling protocols produced a positioning algorithm, capable of fusing the system received
signal strength parameters received from multiple systems and multiple operators. Knowing
that most of the areas are covered by signals from more than one network operator and even
more than one system from one operator, it becomes easy to note the great practical value of
this novel algorithm. On the other hand, an extensive drive-test measurement campaign,
covering more than 600km in the central areas of Belgrade, was performed. Using this algorithm and applying the single ANN models to the recorded measurements, a 59m average
distance error and 50m median distance error were obtained. Moreover, the positioning in
indoor environment was verified and the degradation of performances, due to the crossenvironment
model use, was reported: 105m average distance error and 101m median distance
error.
When applying the new, cascade-connected ANN structure model, distance errors were
reduced to 26m and 2m, for the average and median distance errors, respectively.
The obtained positioning accuracy was shown to be good enough for the implementation of a
broad scope of location based services by using the existing and deployed, commonly
available, infrastructure
WLAN Location Sharing through a Privacy Observant Architecture
In the last few years, WLAN has seen immense growth and it will continue this trend due to the fact that it provides convenient connectivity as well as high speed links. Furthermore, the infrastructure already exists in most public places and is cheap to extend. These advantages, together with the fact that WLAN covers a large area and is not restricted to line of sight, have led to developing many WLAN localization techniques and applications based on them. In this paper we present a novel calibration-free localization technique using the existing WLAN infrastructure that enables conference participants to determine their location without the need of a centralized system. The evaluation results illustrate the superiority of our technique compared to existing methods. In addition, we present a privacy observant architecture to share location information. We handle both the location of people and the resources in the infrastructure as services, which can be easily discovered and used. An important design issue for us was to avoid tracking people and giving the users control over who they share their location information with and under which conditions
INDOOR LOCATION SENSING USING WLAN
The purpose of this project was to produce an indoor location system using
WLAN IEEE 802.11b in Access Point (AP) infrastructure setup without additional
external hardware. The system was developed in MATLAB platform that comprises of
location sensing algorithm (Nearest Neighbor), signal strength database, client-server
interface and graphical user interface (GUI). The basic idea was to build a database of
signal strength information for various predetermined locations. Using the Nearest
Neighbor algorithm, this database can be used to determine a particular location provided
that a set of signal strength is given. As for the location sensing system's performance, it
had a precision in terms of average distance error of 5 m and an accuracy of 72.2 %. The
justification of these results was that noise, interference, multi-path effect, indoor
propagation loss and random movement in the environment affectedRF signals
Indoor Mobile Positioning Using Neural Networks and Fuzzy Logic Control
Indoor mobile navigation systems are becoming more prevalent in many areas (transport, public institutions, logistics, etc.). The interior navigation based on the access points, arranged according to the radio fingerprints, is becoming increasingly popular. The model of artificial neural networks (ANN) is often used as a mechanism for storing and processing radio fingerprints. The task of selection of the access point in WLAN network in the case of high user density is quite topical. Such selection must take into account not only the level of the signal received by the mobile device, but also a width in the dedicated channel bandwidth. The main issues related to the creation of program complex for the mobile indoors navigation using neural networks is discussed in the chapter as well as the method of access point selection based on analysis not only the signal level but also the other parameters. To solve this task, fuzzy logic is used
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