A framework and analysis for cooperative search using UAV swarms
Proceedings of the 2004 ACM symposium on Applied computing
Vision-based Target Geo-location using a Fixed-wing Miniature Air Vehicle
Journal of Intelligent and Robotic Systems
Cooperative control of UAVs for localization of intermittently emitting mobile targets
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on cybernetics and cognitive informatics
Decentralised ground target tracking with heterogeneous sensing nodes on multiple UAVs
IPSN'03 Proceedings of the 2nd international conference on Information processing in sensor networks
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One of the current unmanned systems research areas at the US Air Force Academy is finding robust methods to locate ground mobile targets using multiple, cooperative unmanned aerial vehicles (UAVs). In our previous work (Plett et al., Lect Notes Control Inf Sci 369:22---44, 2007), we showed an effective method to search, detect, and localize static ground targets. The current focus of our research is to extend the method to handle mobile ground targets. To that end, we introduced a novel concept called Sensor Fusion Quality (SFQ) in Kwon and Pack (2011). In this paper, we adapt and incorporate the SFQ principle to include both static and mobile ground targets in a modified Out-of-Order Sigma Point Kalman Filtering (O3SPKF) approach (Plett et al., Lect Notes Control Inf Sci 369:22---44, 2007). The proposed method uses augmented covariances of sigma points to compute SFQ values. This approach showed superior performance over those observed when either the SFQ method or the O3SPKF method was used alone. The added benefit of the integrated approach is in the reduction of computational complexity associated with the propagation updates of target state uncertainties. We validate the proposed method using both simulation and flight experiment results.