| Citation: | Ye Wenbo, Xiong Bangshu, Li Wei, et al. Helicopter flight state recognition method based on temporal attention and state transition constraints[J]. Journal of Aerospace Power, 2026, 41（X）：20260069 doi: 10.13224/j.cnki.jasp.20260069
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To address the issue that existing flight state recognition methods fail to consider the temporal sequence and regularity of flight, resulting in insufficient recognition accuracy under non-stationary flight conditions, a helicopter flight state recognition method based on temporal attention and state transition constraints was proposed. A multi-channel perceptual residual module was introduced to effectively extract key representation information from flight parameters. A temporal attention module was designed to capture the correlation of flight parameters over time. A Markov state transition constraint module was constructed, combining state transition priors to output continuous and flight-regulated state recognition results. Experiments on a flight parameter dataset collected from an actual helicopter flight indicated that, compared with mainstream methods, the proposed method improved the precision, recall, and F1 score of state recognition under non-stationary flight conditions by 2.88%, 2.73%, and 2.81%, respectively. The model achieved a single inference time of 23 ms and a computational cost of 20 Mflops, thus demonstrating significant engineering application value.
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