Specific Emitter Identification Research Articles

Research on an Enhanced Multimodal Network for Specific Emitter Identification

Specific emitter identification (SEI) refers to the task of distinguishing similar emitters, especially those of the same type and transmission parameters, which is one of the most critical tasks of electronic warfare. However, SEI is still a challenging task when a feature has low physical representation. Feature representation largely determines the recognition results. Therefore, this article expects to move toward robust feature representation for SEI. Efficient multimodal strategies have great potential for applications using multimodal data and can further improve the performance of SEI. In this research, we introduce a multimodal emitter identification method that explores the application of multimodal data, time-series radar signals, and feature vector data to an enhanced transformer, which employs a conformer block to embed the raw data and integrates an efficient multimodal feature representation module. Moreover, we employ self-knowledge distillation to mitigate overconfident predictions and reduce intra-class variations. Our study reveals that multimodal data provide sufficient information for specific emitter identification. Simultaneously, we propose the CV-CutMixOut method to augment the time-domain signal. Extensive experiments on real radar datasets indicate that the proposed method achieves more accurate identification results and higher feature discriminability.

Specific Emitter Identification Using Adaptive Signal Feature Embedded Knowledge Graph

Specific Emitter Identification Using Adaptive Signal Feature Embedded Knowledge Graph

Specific Emitter Identification Based on Attractor Feature Space of System under Blind Equalization

In the process of the individual identification of radiation sources, the effective extraction of fine features of target radiation sources can be regarded as being crucial for the subsequent individual identification. However, in the complex electromagnetic environment, the effective extraction of the radiation source features is still facing great challenges. To solve this problem, we propose an algorithm for constructing the attractor feature space of the radiation source system based on blind equalization to solve this problem. Firstly, we use blind equalization to process the target signal. Secondly, we use the phase-space reconstruction technique to construct the system attractor feature space of the target signal processed, and explore the adaptive relationship between feature-space-embedding dimensions, the delay time and the neural network, finding the optimal values. Finally, the feature space is used as the input of the neural network for the subsequent individual discrimination decision. Experimentally, it is proved that our proposed algorithm improves the individual recognition rate of the target radiation source under the complex electromagnetic environment to a certain extent, which is of practical application value.

Generative adversarial networks with Gramian angular field for handling imbalanced data in specific emitter identification

Generative adversarial networks with Gramian angular field for handling imbalanced data in specific emitter identification

Specific Emitter Identification With Different Transmission Codes and Multiple Receivers

Specific Emitter Identification With Different Transmission Codes and Multiple Receivers

Adversarial Attack Generation based on Meta Learning in Specific Emitter Identification

Adversarial Attack Generation based on Meta Learning in Specific Emitter Identification

GLR-SEI: Green and Low Resource Specific Emitter Identification Based on Complex Networks and Fisher Pruning

GLR-SEI: Green and Low Resource Specific Emitter Identification Based on Complex Networks and Fisher Pruning

Open-Set Specific Emitter Identification Leveraging Enhanced Metric Denoising Auto-Encoders

Open-Set Specific Emitter Identification Leveraging Enhanced Metric Denoising Auto-Encoders

GP-DGECN: Geometric Prior Dynamic Group Equivariant Convolutional Networks for Specific Emitter Identification

GP-DGECN: Geometric Prior Dynamic Group Equivariant Convolutional Networks for Specific Emitter Identification

Enhanced Few-Shot Specific Emitter Identification via Phase Shift Prediction and Decoupling

Enhanced Few-Shot Specific Emitter Identification via Phase Shift Prediction and Decoupling

Specific Emitter Identification Based on Multi-Scale Multi-Dimensional Approximate Entropy

Specific Emitter Identification Based on Multi-Scale Multi-Dimensional Approximate Entropy

LT-SEI: Long-Tailed Specific Emitter Identification Based on Decoupled Representation Learning in Low-Resource Scenarios

LT-SEI: Long-Tailed Specific Emitter Identification Based on Decoupled Representation Learning in Low-Resource Scenarios

Robust Specific Emitter Identification With Sample Selection and Regularization Under Label Noise

Robust Specific Emitter Identification With Sample Selection and Regularization Under Label Noise

Fast Fourier Transform With Multihead Attention for Specific Emitter Identification

Fast Fourier Transform With Multihead Attention for Specific Emitter Identification

A Robust Specific Emitter Identification Method for CPS Devices Based on Deep Residual Shrinkage Network

A Robust Specific Emitter Identification Method for CPS Devices Based on Deep Residual Shrinkage Network

Feature-Imitation Federated Learning: An Efficient Approach for Specific Emitter Identification in Low-Resource Environments

Feature-Imitation Federated Learning: An Efficient Approach for Specific Emitter Identification in Low-Resource Environments

Adaptive Decomposition and Extraction Network of Individual Fingerprint Features for Specific Emitter Identification

Adaptive Decomposition and Extraction Network of Individual Fingerprint Features for Specific Emitter Identification

A Zynq-Based Platform With Conditional- Reconfigurable Complex-Valued Neural Network for Specific Emitter Identification

A Zynq-Based Platform With Conditional- Reconfigurable Complex-Valued Neural Network for Specific Emitter Identification

A Class-Incremental Approach With Self-Training and Prototype Augmentation for Specific Emitter Identification

A Class-Incremental Approach With Self-Training and Prototype Augmentation for Specific Emitter Identification

Joint Variational Modal Decomposition for Specific Emitter Identification With Multiple Sensors

Joint Variational Modal Decomposition for Specific Emitter Identification With Multiple Sensors