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Most existing air target intention recognition methods use only single-moment information, risking failure when acquiring data containing noise and many outliers. The robustness of methods that utilize continuous moment information has yet to be explored. This paper designs a robust recognition method for air target intention to address the above problems. The method takes data with noise and outliers as the object, based on a parallel time-channel Transformer Encoder and a weight self-learning unit. First, a detailed introduction to air target intention recognition and robust recognition is given, and the intention space and feature space are defined. Subsequently, the data samples are reconstructed using a fixed-step sliding window to increase the information utilized with multi-moment information as input. Finally, step-wise and channel-wise correlations are extracted using a time-axis Transformer Encoder and a channel-axis Transformer Encoder, respectively, and the weights of the two branches are automatically learnt using a weight self-learning unit. This enhanced self-attention network allocates attention weights between elements in the time and channel domain sequences to capture their long-range and short-range relationships and extract recognizable representations, making it robust to outliers and noise. The experimental results show that the model&#x2019;s recognition accuracy and composite F1 score reach 96.9% and 0.9676, and its performance remains well when the noise level and outliers proportion increase. The ablation and comparison experiments show its advantage in accuracy over other models.

Robust Air Target Intention Recognition Based on Weight Self-learning Parallel Time-channel Transformer Encoder