|13||Development of Indoor Localization Techniques|
This article deals with the implementation of an Android application to ensure Indoor localization of the consumer. To achieve this goal, we introduced a new localization technique that exploits the characteristics of Wireless Sensors Network. Indeed, the proposed technique allows a dramatically distance reduce between the user and different access points and this is achieved by the knowledge of the transmitted signal. Furthermore, fairly reliable target localization has been performed through the triangulation method. Such a contribution could solve several localization problems.
|72||Study of Array Processing Methods for a RADAR System|
Antennas array processing is a very important area and covers several applications in radar, military and telecommunications. This paper discusses the problem of direction of arrival (DOA) estimation for a primary radar signal. An approach to use this type of radar signal in a smart antenna system will be presented and several DOA algorithms will be used. Simulation results verify the usefulness of our methods.
|103||Indoor Localization based on feed-forward Neural Networks and CIR Fingerprinting Techniques|
In indoor, knowing the position of persons and/or equipment is an important safety measure that reduces risks and improves the security of that facility. Being an indoor environment, wireless transmitted signals suffer multiple kinds of distortions due to extreme multipath and non-line of sight (NLOS) conditions. Many solutions to accurate localization in such challenging environments are based on extracting the channel impulse response (CIR) of the received signal and using the fingerprinting technique combined with neural networks (NN). Since these solutions are developed based on neural networks, the choice of the structure of the latter is important in order to provide more precise and accurate results. The principal aim of this work is to identify the neural network model most suitable for indoor localization. For this purpose, different structures are tested and five training algorithms are compared and discussed order to find the most appropriate.