| Citation: | WANG X L，YIN H，DING J F. Aircraft surveillance data fusion method in flight area based on Trans-Attention[J]. Journal of Beijing University of Aeronautics and Astronautics，2025，51（4）：1215-1223 （in Chinese） doi: 10.13700/j.bh.1001-5965.2023.0234
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An aircraft surveillance data fusion method based on a Transformer and attention mechanism is proposed to address the issues of low monitoring accuracy and position jump in a single surveillance source for aircraft in the flight area. Prior to assigning weight values to various surveillance sources via the attention mechanism, features are first extracted from each surveillance source data using the Transformer’s encoder structure. Finally, regression calculations are performed through a fully connected network to obtain the final fusion result. The multilateration (MLAT) data are employed as actual tags, while the surveillance data from the autonomous dependent surveillance-broadcast (ADS-B) system and the surface movement radar (SMR) are chosen as fusion sources. The experimental results show that the proposed method effectively reduces the surveillance error of a single surveillance source, and the fusion effect is better than that of the long short-term memory network based on the attention mechanism, recurrent neural network, and extended Kalman filter fusion methods. The mean absolute error is increased by 2.81%, 16.73% and 35.80% respectively.
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