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MCNN-TEAN based unbalanced HRRP target identification method

Published: 11 October 2024 Publication History

Abstract

The High Resolution Range Profile (HRRP) contains rich target information and is commonly utilized in the field of radar automatic target recognition. Most of the key targets of concern in practical applications are non-cooperative, making it challenging to acquire data for such targets. This often results in an imbalance in target categories within datasets, thereby impacting the classification performance of models. To address these issues, this paper proposes the Weighted-SMOTE (Synthetic Minority Over-sampling Technique) algorithm. The algorithm assigns different weights to the number of synthetic new samples for each sample based on the Euclidean distance between minority class samples and the remaining samples. Furthermore, it utilizes the MCNN-TEAN (Multi-scale Convolutional Neural Network – TransEncoder Attention Network) model to extract multi-level features and capture long-term dependencies between feature dimensions, thereby improving the classification accuracy of targets.

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      SPML '24: Proceedings of the 2024 7th International Conference on Signal Processing and Machine Learning
      July 2024
      353 pages
      ISBN:9798400717192
      DOI:10.1145/3686490
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      Published: 11 October 2024

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      Author Tags

      1. Data Imbalance
      2. HRRP
      3. MCNN-TEAN
      4. Weighted-SMOTE

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      • Key Programs of the National Natural Foundation of China

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