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Generating Synthetic Training Data for Deep Learning-Based UAV Trajectory Prediction
To demonstrate the applicability of the synthetic trajectory data, we show that an RNN-based prediction model solely trained on the generated data can outperform classic reference models on a real-world UAV tracking dataset.
MissFormer: (In-)attention-based handling of missing observations for trajectory filtering and prediction
By providing missing tokens, binary-encoded missing events, the model learns to in-attend to missing data and infers a complete trajectory conditioned on the remaining inputs.
Handling Missing Observations with an RNN-based Prediction-Update Cycle
For providing a full temporal filtering cycle, a basic RNN is extended to take observations and the associated belief about its accuracy into account for updating the current state.
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