16,590 research outputs found
Location-aware computing: a neural network model for determining location in wireless LANs
The strengths of the RF signals arriving from more access points in a wireless LANs are related to the position of the mobile terminal and can be used to derive the location of the user. In a heterogeneous environment, e.g. inside a building or in a variegated urban geometry, the received power is a very complex function of the distance, the geometry, the materials. The complexity of the inverse problem (to derive the position from the signals) and the lack of complete information, motivate to consider flexible models based on a network of functions (neural networks). Specifying the value of the free parameters of the model requires a supervised learning strategy that starts from a set of labeled examples to construct a model that will then generalize in an appropriate manner when confronted with new data, not present in the training set. The advantage of the method is that it does not require ad-hoc infrastructure in addition to the wireless LAN, while the flexible modeling and learning capabilities of neural networks achieve lower errors in determining the position, are amenable to incremental improvements, and do not require the detailed knowledge of the access point locations and of the building characteristics. A user needs only a map of the working space and a small number of identified locations to train a system, as evidenced by the experimental results presented
A Novel Path Prediction Strategy for Tracking Intelligent Travelers
There are various technologies for positioning and tracking of intelligent travelers such
as wireless local area networks (WLAN). However, the loss of actual positioning data is
a common problem due to unexpected disconnection between tracking references and
the traveler. Disconnection of the mobile terminal (MT) from the access points (AP) in
WLAN-based systems is the example case of the problem. While enhancement of the
physical system itself can reduce the risk of disconnections, complementary algorithms
provide even more robustness in localization and tracking of the traveler.
This research aims to develop a novel path prediction system which could keep track of
the traveler during temporary shortage of actual positioning data. The system takes the
advantage of the past trajectory information to compensate for the missing information
during disconnections. A novel decision support system (DSS) is devised with the
ability of learning decisional as well as kinematical behaviors of intelligent travelers. The system is then used in path prediction mode for reconstructing the missing parts of
the trajectory when actual positioning data is unavailable.
An ActivMedia Pioneer robot navigating under fuzzy artificial potential fields (APF)
and blind-folded human subjects are the two types of intelligent travelers. The reactive
motion of robots and path planning strategies of the blinds are similar in that both of
them locally acquire knowledge and explore the space based on route-like spatial
cognition. It is proposed and shown that route-like intelligent motion is based on a
combination of decisional and kinematical factors. The system is designed in such a way
to integrate these two types of motion factors using causal inference mechanism of the
fuzzy cognitive map (FCM). The FCM nodes are a novel selection of kinematical
factors. Genetic algorithm (GA) is then used to train the FCM to be able to replicate the
decisional behaviors of the intelligent traveler.
Experimental works show the capabilities of the developed DSS in human path
prediction using both simulated and actual WLAN-based positioning dataset. Locational
error is set to be limited to 1 m which is suitable for wireless tracking of human subjects
with up to 10% improvement compared to the most related works. Both simulation and
actual experiments were also carried out on the Pioneer platform. The accuracy in
prediction of robot trajectory was obtained about 83% with considerable improvement
compared to the recent methods. Apart from the positioning algorithm of this
dissertation, there are several applications of this DSS to other areas including assistive
technology for the blind and human-robot interaction
Statistical Approaches for Initial Access in mmWave 5G Systems
mmWave communication systems overcome high attenuation by using multiple
antennas at both the transmitter and the receiver to perform beamforming. Upon
entrance of a user equipment (UE) into a cell a scanning procedure must be
performed by the base station in order to find the UE, in what is known as
initial access (IA) procedure. In this paper we start from the observation that
UEs are more likely to enter from some directions than from others, as they
typically move along streets, while other movements are impossible due to the
presence of obstacles. Moreover, users are entering with a given time
statistics, for example described by inter-arrival times. In this context we
propose scanning strategies for IA that take into account the entrance
statistics. In particular, we propose two approaches: a memory-less random
illumination (MLRI) algorithm and a statistic and memory-based illumination
(SMBI) algorithm. The MLRI algorithm scans a random sector in each slot, based
on the statistics of sector entrance, without memory. The SMBI algorithm
instead scans sectors in a deterministic sequence selected according to the
statistics of sector entrance and time of entrance, and taking into account the
fact that the user has not yet been discovered (thus including memory). We
assess the performance of the proposed methods in terms of average discovery
time
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
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