WVEmbs with its Masking: A Method For Radar Signal Sorting
This addresses radar signal processing for defense/communication systems, but appears incremental as it builds on existing neural network approaches with specific adaptations.
The paper tackles the problem of sorting interleaved radar signals by proposing Wide-Value-Embeddings (WVEmbs) with a masking method to handle feature imbalance, achieving high-granularity pulse sorting in complex environments.
Our study proposes a novel embedding method, Wide-Value-Embeddings (WVEmbs), for processing Pulse Descriptor Words (PDWs) as normalized inputs to neural networks. This method adapts to the distribution of interleaved radar signals, ranking original signal features from trivial to useful and stabilizing the learning process. To address the imbalance in radar signal interleaving, we introduce a value dimension masking method on WVEmbs, which automatically and efficiently generates challenging samples, and constructs interleaving scenarios, thereby compelling the model to learn robust features. Experimental results demonstrate that our method is an efficient end-to-end approach, achieving high-granularity, sample-level pulse sorting for high-density interleaved radar pulse sequences in complex and non-ideal environments.