Radar working mode recognition based on self-attention multi-kernel dilated convolution network

Abstract:

In complex electromagnetic environments, radar countermeasure reconnaissance signals often suffer from significant distribution differences between training data and actual combat scenarios due to substantial pulse loss and false pulse interference, which seriously degrades the recognition accuracy of the air-to-air working mode of active phased array radar. To address this issue, this paper proposes a recognition model based on self-attention multi-kernel dilated convolution network (SAMKDCN). Centered on dilated convolution, multi-kernel selection, and residual structures, this model constructs a feature-map extraction module for multi-scale feature learning across the temporal dimension. Moreover, a self-attention mechanism is incorporated to adaptively adjust feature-map weights, thereby highlighting critical features and strengthening feature representation, which ultimately enhances the accuracy of AESA radar working-mode identification. Simulation experiments show that SAMKDCN can effectively learn the core features of the air-to-air working mode of AESA radar. Under ideal conditions, it achieves a peak accuracy of 99.14%. With pulse-loss and false-pulse ratios ranging from 0% to 50%, the average recognition rate attains 95.11%; Even under the extreme scenario of 50% loss rate and 50% false-pulse rate, this model retains a recognition accuracy of 88.23%, demonstrating favorable generalization ability and robustness.