A Radar Source Identification Method Based on Dual-Branch Fusion and Variable-Length Sequence Processing

Abstract

This invention discloses a radar source identification method based on dual-branch fusion and variable-length sequence processing. The method first acquires radar pulse descriptor sequences to construct a standardized dataset. Then, zero-padding is performed on variable-length sequences from the same batch of the dataset, and a binary mask matrix is ​​simultaneously generated to distinguish valid pulses from zero-padding positions. In the feature extraction stage, a temporal branch and an adaptive residual convolutional branch are constructed: the temporal branch utilizes a bidirectional long short-term memory network combined with a length-aware temporal attention mechanism to extract global features; the adaptive residual convolutional branch uses multi-scale convolutional kernels to extract local structural features and employs a mask propagation mechanism to suppress padding interference. Finally, the features from both branches are concatenated and fused, and the prediction result is output through a multi-level fully connected classification network. This invention effectively solves the noise interference problem in variable-length sequence processing and significantly improves the accuracy and robustness of radar source identification in complex environments.

Images