Analysis of WLAN-based Indoor Positioning Methods

OData support
Supervisor:
Dr. Huszák Árpád
Department of Networked Systems and Services

Nowadays, people have more and more devices which are connected to the network. These

devices are with us most of the day and can be easily accessed providing numbers of different

functions. One of these is the GPS, which makes it possible to receive the signals of satellites

and locate a position or design a route. However, we have these intelligent technologies,

using the mentioned function indoor is still in question. Unfortunately satellite sign can not

be sensed indoor, but in many cases it would be very useful to reach its functionality. One

of the cases is navigating in parking garage. It might be wondering, why is it important to

plan a route in a parking garage? The answer is more simple than we thought, because if the

garage has an average occupancy, then the time until a car navigate to the best, free place

(for example, near to the entrance of the cinema) by own, it emits significantly more harmful

material than navigated by computer aided algorithm. Not to mention the fact that its own

costs and extra stress can be reduced.

Another feature besides the GPS is the WiFi, which is also available on most mobile devices

and does not require administrative subscription. With this technology and using a well

optimized algorithm, our system can compete with the benefits of the GPS functionality.

My essay based on the Alle shopping centers WiFi supported parking garage system called

iParking. I made simulations and tested custom positioning algorithms. My final goal was to

refine positioning algorithm estimates and to receive demonstrable better results.

From the commonly used methods I choose the fingerprint technique group for starting

point. I chose Matlab Works for the simulations, which has a lot of positive facility such as

K-nearest-neighbor search (KNN-search) algorithm, which use the pattern-fitting method

in order to find the best fitting received signal strength values and estimate the actual

mobile terminal position. The signal level database (fingerprint) was filled up with real

measurements, which are serving as reference points for the positioning algorithm. In my

essay I worked out and presented estimating improver methods, which are extensions of

the base KNN search for receiving better results. By calculating the average of the returned

positions of the KNN algorithm, using location weighs, route mapping methods, I received

better results. Next idea was to use memory and limitation of decisions by distance. In order

to evaluate the efficiency of the extension methods I introduced an index number to show the

discrepancy of the results.

Using the above mentioned methods, I compared the simulations and measure results using

different parameter setups. By analyzing the variations of the results, I made very interesting

conclusions about the presented indoor positioning methods.

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