Specific Emitter Identification Research Articles

Multi-scale iterative domain adaptation for specific emitter identification

Multi-scale iterative domain adaptation for specific emitter identification

Attention-Enhanced Dual-Branch Residual Network with Adaptive L-Softmax Loss for Specific Emitter Identification under Low-Signal-to-Noise Ratio Conditions

To address the issue associated with poor accuracy rates for specific emitter identification (SEI) under low signal-to-noise ratio (SNR) conditions, where the single-dimension radar signal characteristics are severely affected by noise, we propose an attention-enhanced dual-branch residual network structure based on the adaptive large-margin Softmax (ALS). Initially, we designed a dual-branch network structure to extract features from one-dimensional intermediate frequency data and two-dimensional time–frequency images, respectively. By assigning different attention weights according to their importance, these features are fused into an enhanced joint feature for further training. This approach enables the model to extract distinctive features across multiple dimensions and achieve good recognition performance even when the signal is affected by noise. In addition, we have introduced L-Softmax to replace the original Softmax and propose the ALS. This approach adaptively calculates the classification margin decision parameter based on the angle between samples and the classification boundary and adjusts the margin values of the sample classification boundaries; it reduces the intra-class distance for the same class while increasing the inter-class distance between different classes without the need for cumbersome experiments to determine the optimal value of decision parameters. Our experimental findings revealed that, in comparison to alternative methods, our proposed approach markedly enhances the model’s capability to extract features from signals and classify them in low-SNR environments, thereby effectively diminishing the influence of noise. Notably, it achieves the highest recognition rate across a range of low-SNR conditions, registering an average increase in recognition rate of 4.8%.

Dual‐labelled multi‐Bernoulli filter based on specific emitter identification

AbstractIn complex electromagnetic environments, airborne passive bistatic radar encounters the challenge of associating emitters with measurements for multi‐target tracking. The authors propose a solution based on specific emitter identification technology. Firstly, generative adversarial networks (GANs) are utilised to extract and classify emitter signals using radio frequency fingerprint (RFF) features. The classification results are then used to construct a set of emitter labels for pre‐processing the measurement data. Subsequently, the pre‐processed measurement data set is input into the labelled multi‐Bernoulli filter framework, which is extended to a dual‐labelled (target label and emitter label) multi‐Bernoulli filter. This filter jointly predicts and updates the multi‐target posterior density, enabling the estimation of multi‐target trajectories. The effectiveness of the proposed algorithm is validated using two experiments. The results demonstrate that the GAN based on RFF features effectively identifies emitter signals. Moreover, the dual‐labelled multi‐Bernoulli filter, based on specific emitter identification, accurately estimates multi‐target trajectories using measurement data from an airborne passive radar of the multi‐transmit single‐receive type. This approach provides a novel and effective solution to the multi‐target tracking problem in complex electromagnetic environments.

Specific emitter identification based on ensemble domain adversarial neural network in multi-domain environments

Specific emitter identification is pivotal in both military and civilian sectors for discerning the unique hardware distinctions inherent to various launchers, it can be used to implement security in wireless communications. Recently, a large number of deep learning-based methods for specific emitter identification have been proposed, achieving good performance. However, these methods are trained based on a large amount of data and the data are independently and identically distributed. In actual complex environments, it is very difficult to obtain reliable labeled data. Aiming at the problems of difficulty in data collection and annotation, and the large difference in distribution between training data and test data, a method for individual radiation source identification based on ensemble domain adversarial neural network was proposed. Specifically, a domain adversarial neural network is designed and a Transformer encoder module is added to make the features obey Gaussian distribution and achieve better feature alignment. Ensemble classifiers are then used to enhance the generalization and reliability of the model. In addition, three real and complex migration environments, Alpine–Montane Channel, Plain-Hillock Channel, and Urban-Dense Channel, were constructed, and experiments were conducted on WiFi dataset. The simulation results show that the proposed method exhibits superior performance compared to the other six methods, with an accuracy improvement of about 3%.

Specific Emitter Identification through Multi-Domain Mixed Kernel Canonical Correlation Analysis

Radar specific emitter identification (SEI) involves extracting distinct fingerprints from radar signals to precisely attribute them to corresponding radar transmitters. In view of the limited characterization of fingerprint information by single-domain features, this paper proposes the utilization of multi-domain mixed kernel canonical correlation analysis for radar SEI. Initially, leveraging the complementarity across diverse feature domains, fingerprint features are extracted from four distinct domains including: envelope feature, spectrum feature, short-time Fourier transform and ambiguity function. Subsequently, kernel canonical correlation analysis is employed to amalgamate the correlation characteristics inherent in multi-domain data. Considering the insufficient of a single kernel function with only interpolation or extrapolation ability, we adopt mixed kernel to improve the projection ability of the kernel function. Experimental results substantiate that the proposed feature fusion approach maximizes the complementarity of multiple features while reducing feature dimensionality. The method achieves an accuracy of up to 95% in experiments, thereby enhancing the efficacy of radar SEI.

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

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

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

Fast Fourier Transform With Multihead Attention for Specific Emitter Identification

Fast Fourier Transform With Multihead Attention 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

Overcoming Data Limitations: A Few-Shot Specific Emitter Identification Method Using Self-Supervised Learning and Adversarial Augmentation

Overcoming Data Limitations: A Few-Shot Specific Emitter Identification Method Using Self-Supervised Learning and Adversarial Augmentation

Enhanced Specific Emitter Identification With Limited Data Through Dual Implicit Regularization

Enhanced Specific Emitter Identification With Limited Data Through Dual Implicit Regularization

Causal Learning for Robust Specific Emitter Identification Over Unknown Channel Statistics

Causal Learning for Robust Specific Emitter Identification Over Unknown Channel Statistics

DTIN: Dual Transformer-based Imputation Nets for multivariate time series emitter missing data

As a kind of multivariate time series (MTS) data, emitter signals often exhibit missing or corrupt values, posing serious challenges to emitter data research such as specific emitter identification (SEI). Existing multivariate missing data imputation (MDI) methods are deficient in two aspects when applied to MTS emitter data: first, single-channel models cannot handle variables with varying numbers of complete samples; second, they cannot efficiently impute MTS data that are out of the model’s domain. To address these issues, a dual channel architecture tailored for MTS emitter data was devised in this study, which is called Dual Transformer-based Imputation Nets (DTIN). DTIN processes different types of variables through two parallel channels to extract different spatiotemporal features. Furthermore, drawing inspiration from image style manipulation, multivariate time series pivotal tuning inversion (MTSPTI) techniques are employed for better imputation performance, in which an in-domain pivotal code is created and input into the generator that is tuned for out-of-domain MTS emitter data. Extensive experiments on two real-world emitter datasets demonstrate that DTIN outperforms several existing MDI models.

Contrastive Self-Supervised Clustering for Specific Emitter Identification

Specific Emitter Identification (SEI) is crucial for attacking and defending Internet of Things (IoT) devices in untrusted scenarios or battlefield environments. However, existing SEI methods usually require annotation information, which is often unavailable in non-cooperative communications and untrusted scenarios. In this paper, we propose a signal contrastive self-supervised clustering (SCSC) method for unsupervised SEI applications. First, we propose SCSC with 1D fingerprint pyramid feature extractor (1D-FPFE) for obtaining hierarchical subtle features of emitter signals. Then, we propose a bit-pulse selection (BPS) strategy and several signal data augmentation methods. By constructing signal positive and negative instance pairs through data augmentation, our approach generates cluster preference representations in a contrastive self-supervised learning manner. Extensive experimental results based on communication burst emitter dataset show that SCSC achieves an accuracy improvement of about 26% over the current best communication signal clustering algorithm. Moreover, SCSC also exhibits good performance and generalization for 30 emitter clustering and few-shot unlabeled signal clustering.