WVEmbs with its Masking: A Method For Radar Signal Sorting

• URL: http://arxiv.org/abs/2503.13480v1
• Date: Wed, 05 Mar 2025 13:47:55 GMT
• Title: WVEmbs with its Masking: A Method For Radar Signal Sorting
• Authors: Xianan Hu, Fu Li, Kairui Niu, Peihan Qi, Zhiyong Liang,
• Abstract summary: We propose a novel embedding method for processing Pulse Descriptor Words (PDWs) as normalized inputs to neural networks.<n>This method adapts to the distribution of interleaved radar signals, ranking original signal features from trivial to useful.<n>We show that our method is an efficient end-to-end approach, achieving high-granularity, sample-level pulse sorting.
• Score: 17.206356892576434
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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