Data Enhancement for Few-shot Radar Countermeasure Reconnaissance via Temporal-Conditional Generative Adversarial Networks

HUANG Linxuan; HE Minghao; YU Chunlai; FENG Mingyue; ZHANG Fuqun; ZHANG Yinan

doi:10.11999/JEIT250280

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HUANG Linxuan, HE Minghao, YU Chunlai, FENG Mingyue, ZHANG Fuqun, ZHANG Yinan. Data Enhancement for Few-shot Radar Countermeasure Reconnaissance via Temporal-Conditional Generative Adversarial Networks[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250280

Citation:

HUANG Linxuan, HE Minghao, YU Chunlai, FENG Mingyue, ZHANG Fuqun, ZHANG Yinan. Data Enhancement for Few-shot Radar Countermeasure Reconnaissance via Temporal-Conditional Generative Adversarial Networks[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250280

Citation:

HUANG Linxuan, HE Minghao, YU Chunlai, FENG Mingyue, ZHANG Fuqun, ZHANG Yinan. Data Enhancement for Few-shot Radar Countermeasure Reconnaissance via Temporal-Conditional Generative Adversarial Networks[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250280

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Data Enhancement for Few-shot Radar Countermeasure Reconnaissance via Temporal-Conditional Generative Adversarial Networks

doi: 10.11999/JEIT250280 cstr: 32379.14.JEIT250280

Air Force Early Warning Academy, Wuhan 430019, China

Received Date: 2025-04-16
Rev Recd Date: 2025-07-22

Available Online: 2025-07-30

Abstract

Abstract

Objective Radar electronic warfare systems rely on Pulse Descriptor Words (PDWs) to represent radar signals, capturing key parameters such as Time of Arrival (TOA), Carrier Frequency (CF), Pulse Width (PW), and Pulse Amplitude (PA). However, in complex electromagnetic environments, the scarcity of PDW data limits the effectiveness of data-driven models in radar pattern recognition. Conventional augmentation methods (e.g., geometric transformations, SMOTE) fall short in addressing three core issues: (1) failure to capture temporal physical laws (e.g., gradual frequency shifts and pulse modulation patterns); (2) distributional inconsistencies (e.g., frequency band overflows and PW discontinuities); and (3) weak coupling between PDWs and modulation types, leading to reduced classification accuracy. This study proposes Time-CondGAN, a temporal-conditional generative adversarial network designed to generate physically consistent and modulation-specific PDW sequences, enhancing few-shot radar reconnaissance performance. Methods Time-CondGAN integrates three core innovations: (1) Multimodal Conditional Generation Framework: A label encoder maps discrete radar modulation types (e.g., VS, HRWS, TWS in Table 2) into 128-dimensional feature vectors. These vectors are temporally expanded and concatenated with latent noise to enable category-controllable generation. The generator architecture (Fig. 4) employs bidirectional Gated Recurrent Units (GRUs) to capture long-range temporal dependencies. (2) Multi-task Discriminator Design: The discriminator (Fig. 5) is designed for joint adversarial discrimination and signal classification. A shared bidirectional GRU with an attention mechanism extracts temporal features, while classification loss constrains the generator to maintain category-specific distributions. (3) Temporal-Statistical Joint Optimization: The training process incorporates multiple loss components: Supervisor Loss (Eq. 7): A bidirectional GRU-based supervisor enforces temporal consistency. Feature Matching Loss (Eq. 9): Encourages alignment between high-level features of real and synthetic signals. Adversarial and Classification Losses (Eqs. (8)–(9)): Promote distribution realism and accurate category separation. A two-stage training strategy is adopted to improve stability, consisting of pre-training (Fig. 2) followed by adversarial training (Fig. 3). Results and Discussions Time-CondGAN demonstrates strong performance across three core dimensions. (1) Physical plausibility is achieved through accurate modeling of radar signal dynamics. Compared with TimeGAN, Time-CondGAN reduces the Kullback-Leibler (KL) divergence by 28.25% on average. Specifically, the PW distribution error decreases by 50.20% (KL = 4.68), the TOA interval error decreases by 20.5% (KL = 0.636), and the CF deviation is reduced by 13.29% (KL = 7.93), confirming the suppression of non-physical signal discontinuities. (2) Downstream task enhancement highlights the model’s few-shot generation capability. With only 10 real samples, classification accuracy improves markedly: VGG16 accuracy increases by 37.2% (from 43.0% to 59.0%) and LSTM accuracy by 28.6% (from 49.0% to 63.0%), both substantially outperforming conventional data augmentation methods. (3) Ablation studies validate the contribution of key modules. Removing the conditional encoder increases the PW KL divergence by 107.4%. Excluding the supervisor loss degrades CF continuity by 76.2%, and omitting feature matching results in a 44.6% misalignment in amplitude distribution. Conclusions This study establishes Time-CondGAN as an effective solution for radar PDW generation under few-shot conditions, addressing three key limitations: temporal fragmentation is resolved through bidirectional GRU supervision, mode collapse is alleviated via multimodal conditioning, and modulation specificity is maintained by classification constraints. The proposed framework offers operational value, enabling over 35% improvement in recognition accuracy under low-intercept conditions. Future work will incorporate radar propagation physics to refine amplitude modeling (current KL = 8.51), adopt meta-learning approaches for real-time adaptation in dynamic battlefield environments, and expand the model to multi-radar cooperative scenarios to support heterogeneous electromagnetic contexts.
- Generative Adversarial Networks (GAN),
- Few-shot learning,
- Radar countermeasure reconnaissance,
- Temporal data generation,
- Pattern recognition

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