Online few-shot learning for multi-function radars mode recognition based on backtracking contextual prototypical memory

Modern multi-function radars (MFRs) can flexibly generate multiple fine-grained working modes for different missions through programmable parameters. Precise recognition of these working modes by analyzing the parameter combinations lays foundation for situational awareness. This recognition becomes more challenging to electromagnetic reconnaissance system when prior information of radiation source is unavailable and effective labeled signal data is not sufficient in adversarial scenarios. The presence of incremental mode involved in radar data flow requires that the recognition model enables to dynamic adjust and online perceive current data. This issue incorporates online learning to few-shot learning to accomplish efficiently recognition to a novel mode with the support of small amount of data, and still retain model's ability to recognize existing modes. This paper designed a backtracking contextual prototypical memory (BCPM) network for online MFR mode recognition. The proposed method learns temporal and spatial information from data stream as a reference predicting the signal segments as existing working modes or a novel mode. The BCPM network also designs a backtracking module and an alignment regularization term to utilize the data without annotation adequately and obtain a more reliable category representation. The experimental results verified the proposed method's good performance and high robustness to non-ideal factors and distractors.