ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Publications Copernicus
Articles | Volume III-1
02 Jun 2016
 | 02 Jun 2016


Salil Goel, Allison Kealy, and Bharat Lohani

Keywords: Cooperative Localization, Unmanned Aerial System, Kalman Filter, Centralized architecture, Distributed architecture, Covariance Intersection

Abstract. Networks of small, low cost Unmanned Aerial Systems (UASs) have the potential to improve responsiveness and situational awareness across an increasing number of applications including defense, surveillance, mapping, search and rescue, disaster management, mineral exploration, assisted guidance and navigation etc. These ad hoc UAS networks typically have the capability to communicate with each other and can share data between the individual UAS nodes. Thus these networks can operate as robust and efficient information acquisition platforms. For any of the applications involving UASs, a primary requirement is the localization i.e. determining the position and orientation of the UAS. The performance requirements of localization can vary with individual applications, for example: mapping applications need much higher localization accuracy as compared to the applications involving only surveillance. The sharing of appropriate data between UASs can prove to be advantageous when compared to a single UAS, in terms of improving the positioning accuracy and reliability particularly in partially or completely GNSS denied environments. This research aims to integrate low cost positioning sensors and cooperative localization technique for a network of UASs. Our hypothesis is that it is possible to achieve high accurate, real-time localization of each of the nodes in the network even with cheaper sensors if the nodes of the network share information among themselves. This hypothesis is validated using simulations and the results are analyzed both for centralized and distributed estimation architectures. At first, the results are studied for a two node network which is then expanded for a network containing more number of nodes. Having more nodes in the network allows us to study the properties of the network including the effect of size and shape of the network on accuracy of the nodes.