Trajectory-BERT: Trajectory Estimation Based on BERT Trajectory Pre-Training Model and Particle Filter Algorithm

In the realm of aviation, trajectory data play a crucial role in determining the target’s flight intentions and guaranteeing flight safety. However, the data collection process can be hindered by noise or signal interruptions, thus diminishing the precision of the data. This paper uses the bidirecti...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2023-11, Vol.23 (22), p.9120
Main Authors: Wu, You, Yu, Hongyi, Du, Jianping, Ge, Chenglong
Format: Article
Language:English
Subjects:
Aircraft
Algorithms
Aviation
BERT
Computational linguistics
Deep learning
Dictionaries
Language processing
maximum posterior probability
Natural language interfaces
Natural language processing
Noise
particle filter
Probability
trajectory
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Summary:In the realm of aviation, trajectory data play a crucial role in determining the target’s flight intentions and guaranteeing flight safety. However, the data collection process can be hindered by noise or signal interruptions, thus diminishing the precision of the data. This paper uses the bidirectional encoder representations from transformers (BERT) model to solve the problem by masking the high-precision automatic dependent survey broadcast (ADS-B) trajectory data and estimating the mask position value based on the front and rear trajectory points during BERT model training. Through this process, the model acquires knowledge of intricate motion patterns within the trajectory data and acquires the BERT pre-training Model. Afterwards, a refined particle filter algorithm is utilized to generate alternative trajectory sets for observation trajectory data that is prone to noise. Ultimately, the BERT trajectory pre-training model is supplied with the alternative trajectory set, and the optimal trajectory is determined by computing the maximum posterior probability. The results of the experiment show that the model has good performance and is stronger than traditional algorithms.
ISSN:1424-8220
1424-8220
DOI:10.3390/s23229120