Multi-Sensor Tracking and Fusion for Space Radar Application
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162 Genesee Street, Utica, NY, 13502
AbstractBlack River proposes a solution for Space Radar Multi-Sensor Tracking and Fusion that considers the specific advantages and challenges offered by LEO and MEO constellations. Our approach to this research topic is three-fold in that we will characterize the expected yield of the radar modes given various scenarios, develop a tracking and fusion methodology, and develop a closed loop end-to-end simulation architecture for performance evaluation. The multi-sensor/mode, multi-target tracking and fusion problem is addressed with a fully featured state-vector that consists of kinematic and pose estimates as well as feature attributes derived from HRR, SAR, and ISAR products. The fully featured state-vector is ideally a blueprint of the target, but in reality it is a sub-sampled vector with more dimensionality than kinematics alone. The advantage of the fully featured state-vector is that it can be used in the track-to-measurement assignment process or in a track-to-track fusion process. Another key component is an architecture that includes a sensor resource manager that is necessary to schedule the specific radar modes while optimizing collections and radar energy over multiple areas of interest.
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