AI RESEARCH
Neural Aided Kalman Filtering for UAV State Estimation in Degraded Sensing Environments
arXiv CS.LG
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ArXi:2604.28107v1 Announce Type: new Accurate state estimation of nonlinear dynamical systems is fundamental to modern aerospace operations across air, sea, and space domains. Online tracking of adversarial unmanned aerial vehicles (UAVs) is especially challenging due to agile nonlinear motion, noisy and sparse sensor measurements, and unknown control inputs; conditions that violate key assumptions of classical Kalman filter variants and degrade estimation performance.