Aerial Target Intention Recognition Method Integrating Information Classification Processing and Multi-scale Embedding Graph Robust Learning with Noisy Labels

SONG Zihao; ZHOU Yan; CAI Yichao; CHENG Wei; YUAN Kai; LI Hui

doi:10.11999/JEIT241074

Volume 47 Issue 5

May 2025

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Article Navigation > Journal of Electronics & Information Technology > 2025 > 47(5): 1418-1433

SONG Zihao, ZHOU Yan, CAI Yichao, CHENG Wei, YUAN Kai, LI Hui. Aerial Target Intention Recognition Method Integrating Information Classification Processing and Multi-scale Embedding Graph Robust Learning with Noisy Labels[J]. Journal of Electronics & Information Technology, 2025, 47(5): 1418-1433. doi: 10.11999/JEIT241074

Citation:

SONG Zihao, ZHOU Yan, CAI Yichao, CHENG Wei, YUAN Kai, LI Hui. Aerial Target Intention Recognition Method Integrating Information Classification Processing and Multi-scale Embedding Graph Robust Learning with Noisy Labels[J]. Journal of Electronics & Information Technology, 2025, 47(5): 1418-1433. doi: 10.11999/JEIT241074

Citation:

SONG Zihao, ZHOU Yan, CAI Yichao, CHENG Wei, YUAN Kai, LI Hui. Aerial Target Intention Recognition Method Integrating Information Classification Processing and Multi-scale Embedding Graph Robust Learning with Noisy Labels[J]. Journal of Electronics & Information Technology, 2025, 47(5): 1418-1433. doi: 10.11999/JEIT241074

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Aerial Target Intention Recognition Method Integrating Information Classification Processing and Multi-scale Embedding Graph Robust Learning with Noisy Labels

doi: 10.11999/JEIT241074 cstr: 32379.14.JEIT241074

Department of Early Warning Intelligence, Early Warning Academy, Wuhan 430019, China

Received Date: 2024-12-05
Rev Recd Date: 2025-03-19

Available Online: 2025-03-28

Publish Date: 2025-05-01

Abstract

Abstract

Objective Aerial Target Intention Recognition (ATIR) predicts and assesses the intentions of non-cooperative targets by integrating information acquired and processed by various sensors. Accurate recognition enhances decision-making, aiding commanders and combatants in steering engagements favorably. Therefore, robust and precise recognition methods are essential. Advances in big data and detection technologies have driven research into deep-learning-based intention recognition. However, noisy labels in target intention recognition datasets hinder the reliability of traditional deep-learning models. To address this issue, this study proposes an intention recognition method incorporating Information Classification Processing (ICP) and multi-scale robust learning. The trained model demonstrates high accuracy even in the presence of noisy labels. Methods This method integrates an ICP network, a cross-scale embedding fusion mechanism, and multi-scale embedding graph learning. The ICP network performs cross-classification processing by analyzing attribute correlations and differences, facilitating the extraction of embeddings conducive to intention recognition. The cross-scale embedding fusion mechanism employs target sequences at different scales to train multiple Deep Neural Networks (DNNs) simultaneously. It sequentially integrates robust embeddings from fine to coarse scales. During training, complementary information across scales enables a cross-teaching strategy, where each encoder selects clean-label samples based on a small-loss criterion. Additionally, multi-scale embedding graph learning establishes relationships between labeled and unlabeled samples to correct noisy labels. Specifically, for high-loss unselected samples, the Speaker-listener Label PropagAtion (SLPA) algorithm refines their labels using the multi-scale embedding graph, improving model adaptation to the class distribution of target attribute sequences. Results and Discussions When the proportion of symmetric noise is 20% (Table 1), the test accuracy of the Cross-Entropy (CE) method exceeds 80%, demonstrating the effectiveness of the ICP network. The proposed method achieves both test accuracy and a Macro F1 score (MF1) above 92%. At higher noise levels—50% symmetric noise and 40% asymmetric noise (Table 1)—the performance of other methods declines significantly. In contrast, the proposed method maintains accuracy and MF1 above 80%, indicating greater stability and robustness. This strong performance can be attributed to: (1) Cross-scale fusion, which integrates complementary information from different scales, enhancing the separability and robustness of fused embeddings. This ensures the selection of high-quality samples and prevents performance degradation caused by noisy labels in label propagation. (2) SLPA in multi-scale embedding graph learning, which stabilizes label propagation even when the dataset contains a high proportion of noisy labels. Conclusions This study proposes an intelligent method for recognizing aerial target intentions in the presence of noisy labels. The method effectively addresses noise label by integrating an ICP network, a cross-scale embedding fusion mechanism, and multi-scale embedding graph learning. First, an embedding extraction encoder based on the ICP network is constructed using acquired target attributes. The cross-scale embedding fusion mechanism then integrates encoder outputs from sequences at different scales, facilitating the extraction of multi-scale features and enhancing the reliability of clean samples identified by the small-loss criterion. Finally, multi-scale embedding graph learning, incorporating SLPA, refines noisy labels by leveraging selected clean labels. Experiments on the ATIR dataset across various noise types and levels demonstrate that the proposed method achieves significantly higher test accuracy and M F1 than other baseline approaches. Ablation studies further validate the effectiveness and robustness of the network architecture and mechanisms.

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References(36)

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