|Abstract:||Cooperative swarms of Unmanned Aerial Systems (UAS) are increasingly being discussed among the researchers due to their diverse applications and their advantages over single nodes. For cooperative swarms to operate autonomously, it is required to have a precise estimation of the position and orientation of each of the node. To provide this estimation, a number of algorithms have been proposed in the literature, all of which can be classified in to two broad categories: Centralized and Distributed. Distributed algorithms have been focus of researchers for quite some time due to their robustness and scalability. Distributed networks can be implemented in two different ways based on the type of the nodes: Peer to Infrastructure (P2I) and Peer to Peer (P2P). In this terminology, ‘Peer’ is referred to as the ‘dynamic node’ and ‘Infrastructure’ is referred to as the ‘static node’. This paper provides an analysis of both P2I and P2P networks using numerical simulations. Further this paper presents a comparison of P2I and P2P networks from a qualitative and quantitative perspective. This paper discusses the strengths and shortcomings of different distributed algorithms from a practical perspective. A new distributed EKF based estimation algorithm is presented in this paper and analyzed using numerical simulations. The new algorithm for a P2P network is then compared with a P2I network. Simulation results demonstrate that although a P2I network generally outperforms a P2P network, after imposing certain constraints in a P2P network, it may be able to achieve accuracy comparable to a P2I network.|
Proceedings of the 29th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2016)
September 12 - 16, 2016
Oregon Convention Center
|Pages:||1125 - 1137|
|Cite this article:||
Goel, Salil, Kealy, Allison, Lohani, Bharat, "Infrastructure vs Peer to Peer Cooperative Positioning: A Comparative Analysis," Proceedings of the 29th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2016), Portland, Oregon, September 2016, pp. 1125-1137.
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