A Cooperative Network Framework for Multi-UAV Guided Ground Ad Hoc Networks

Cooperative ad hoc networks are becoming very important in various military and civilian applications. The interfacing between different ad hoc networks provides large applications in field of surveillance, navigation, disaster monitoring and homeland security. This paper focuses on implementation of UAV (unmanned aerial vehicles) ad hoc network that forms a guidance system for ground ad hoc network. The network framework proposed in the paper uses neural network to form cognitive and topology maps. Indirect and Bayesian Kalman Filter are used for estimations. These estimations allows updating of pre-constructed cognitive map to form ideal final search map that is shared among all nodes to perform search and track operations. The analysis showed that the proposed framework is capable of forming a search maps that is able to define multiple way points for each UAV in the network to follow a non-redundant path for searching and identifying various user nodes and geographical territories. The effectiveness of the model is demonstrated using simulations.