A model integrates time-frequency and spatial features to enhance UAV trajectory prediction accuracy, indicating significant improvements over existing methods.
Flight trajectory prediction(FTP) with high precision is the core technology for the autonomous flight of quadrotor unmanned aerial vehicles (UAVs) in environments with limited navigation signals. In response to the problem that most existing methods focus on the features of a single domain and ignore the cross-domain feature correlation, making it challenging to maintain high accuracy in FTP, a model based on Time-Frequency-Spatial Network named TiFSN is proposed. Firstly, based on wavelet transform (WT), the velocity signal is extended to time-frequency joint features. Furthermore, a fusion mechanism is established to construct a multi-domain feature set. Finally, an extended channels-based temporal convolutional network (EC-TCN) is designed, which achieves high-precision FTP. Experiments were conducted on real flight datasets, and the results show that the model significantly improved the evaluation metrics compared to baseline methods.
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Zhao et al. (2026) studied this question.
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