Network framework for multi UAV guided ground adhoc network

Abstract

Unmanned aerial vehicles (UAVs) have gained lots of potential over the years, providing a vast range of applications in existing networks. UAVs have the ability to cooperate, and can fly autonomously or can be operated without human intervention, thus, provides a versatile, and a flexible implementation. Integration of these vehicles can solve various issues concerning both civilian activities as well as military operations. One of the possible integrations of these vehicles is with ad hoc networks. Ad hoc networks are low cost infrastructure based networks that operate on the ideology of forming intermittent networks on demand. Traditional ad hoc networks do not require any centralized device for data forwarding. Every node themselves act as transmitter, receiver, and router. Formation of collaborative network between the aerial nodes and the ground nodes provides a vast range of applications in areas of civilian and military activities. These collaborations can lead to formation of guidance systems that can be used to provide look ahead information to ground units. The work presented in this thesis considers the collaboration between these two different ad hoc units as a research aspect, and provides efficient strategies for enhanced transmission. In the initial phase, a cooperative framework is developed which forms a guidance system comprising of aerial and ground nodes operating in ad hoc mode. This framework forms a collaborative task oriented network, which carries search and tracking without any redundancy. The operating time of this framework is quite low. Also, it acts as a guidance system for ground nodes, thus, providing information of user stations on the ground. The proposed framework utilizes the Bayesian probabilistic model, neural networks, and Quaternion Kalman Filter for its successful operations. The proposed framework acts as a base for network formation between the aerial and the ground nodes.