In recent times smart devices have attracted a large number of users. Since many of these devices allow position estimation using Global Navigation Satellite Systems (GNSS) signals, a large number of location-based applications and services have emerged, especially in transport systems. However GNSS signals are affected by the environment and are not always present, especially in dense urban environment or indoors. In this work firstly a Modular Localization Algorithm is proposed to allow seamless switching between different positioning modules. This helps us develop a positioning system that is able to provide position estimates in both indoor and outdoor environments without any user interaction. Since the proposed system can run as a service on any smart device, it could allow users to navigate not only in outdoor environments, but also indoors, e.g., underground garages, tunnels etc. Secondly we present the proposal of a 2-phase map reduction algorithm which allows one to significantly reduce the complexity of position estimation processes in case that positioning is performed using a fingerprinting framework. The proposed 2-phase map reduction algorithm can also improve the accuracy of the position estimates by filtering out reference points that are far from the mobile device. Both algorithms were implemented into a positioning system and tested in real world conditions in both indoor and outdoor environments.

Localization is an important and extensively studied problem in wireless ad hoc networks. The process of position discovery can be realized utilizing range measurements including received signal strength, time of arrival or angle of arrival. We focus on localization method based on angle of arrival information between neighbor nodes. This paper proposes an original positioning algorithm to improve positioning accuracy of angle of arrival (AoA) methods. It is called Complex Angle of Arrival (CAoA). Simulations show that CAoA achieves better positioning results compared to conventional AoA method. Index Terms-Ad hoc network, Angle of Arrival, Complex Angle of Arrival, blindfolded node, positioning, reference node.

Recently positioning services are getting more attention not only within research community but also from service providers. From the service providers point of view positioning service that will be able to work seamlessly in all environments, for example, indoor, dense urban, and rural, has a huge potential to open new markets. However, such system does not only need to provide accurate position estimates but have to be scalable and resistant to fake positioning requests. In the previous works we have proposed a modular system, which is able to provide seamless positioning in various environments. The system automatically selects optimal positioning module based on available radio signals. The system currently consists of three positioning modules—GPS, GSM based positioning, and Wi-Fi based positioning. In this paper we will propose algorithm which will reduce time needed for position estimation and thus allow higher scalability of the modular system and thus allow providing positioning services to higher amount of users. Such improvement is extremely important, for real world application where large number of users will require position estimates, since positioning error is affected by response time of the positioning server.

Abstract:In this paper, we deal with the improvement of the positioning accuracy of distance vector (DV)-based positioning algorithms, which use angular information DV-angle of arrival (AoA). This algorithm belongs to an adhoc positioning system. We focus on the improvement of the algorithm and its enhanced version is presented. The angular information of particular nodes is obtained and processed by the original algorithm; however, the final position estimation is determined by the proposed novel algorithm using only a subset of all intersections. We assign weights to the individual intersections according to their positions and the positions of the RNs. For the final position estimation, only intersections with the highest weights are used. The performance of the proposed enhanced algorithm is verified by simulations and it is compared with the original DV-AoA algorithm.

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