Abstract
This chapter outlines our research efforts toward developing a cooperative target localization method based on multiple autonomous unmanned aerial vehicles (UAVs) that are outfitted with heterogeneous sensors. The current focus of the research includes (1) optimizing the UAV trajectories to place them at desired locations at desired times to capture target locations, (2) cooperative sensor scheduling, and (3) intelligent fusing of multiple sensor measurements to accurately estimate the position and velocity of a target. The focus of this paper is the sensor-fusion task. One might consider addressing this problem using some form of Kalman filter. However, a complicating factor in the present application is that sensor readings arrive out-of-sequence to the sensor-fusion process. For example, there is non-deterministic latency in the inter- and intra-UAV communication channels. We address this problem by developing an out-of-order sigma-point Kalman filter (O3SPKF).
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Plett, G.L., Zarzhitsky, D., Pack, D.J. (2007). Out-of-Order Sigma-Point Kalman Filtering for Target Localization Using Cooperating Unmanned Aerial Vehicles. In: Pardalos, P.M., Murphey, R., Grundel, D., Hirsch, M.J. (eds) Advances in Cooperative Control and Optimization. Lecture Notes in Control and Information Sciences, vol 369. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74356-9_2
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DOI: https://doi.org/10.1007/978-3-540-74356-9_2
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