Emitter Identification Research Articles

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

Specific Emitter Identification With Different Transmission Codes and Multiple Receivers

Specific Emitter Identification With Different Transmission Codes and Multiple Receivers

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

KG-IBL: Knowledge Graph Driven Incremental Broad Learning for Few-Shot Specific Emitter Identification

KG-IBL: Knowledge Graph Driven Incremental Broad Learning for Few-Shot Specific Emitter Identification

Adversarial Attack Generation based on Meta Learning in Specific Emitter Identification

Adversarial Attack Generation based on Meta Learning in 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

Towards Open-Set Specific Emitter Identification Using Auxiliary Classifier Generative Adversarial Network and OpenMax

Towards Open-Set Specific Emitter Identification Using Auxiliary Classifier Generative Adversarial Network and OpenMax

Causal Learning for Robust Specific Emitter Identification Over Unknown Channel Statistics

Causal Learning for Robust Specific Emitter Identification Over Unknown Channel Statistics

A Self-Supervised-Based Approach of Specific Emitter Identification for the Automatic Identification System

A Self-Supervised-Based Approach of Specific Emitter Identification for the Automatic Identification System

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.

Interpolative Metric Learning for Few-Shot Specific Emitter Identification

Interpolative Metric Learning for Few-Shot Specific Emitter Identification

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.

Assessment of identification performance for high emission heavy-duty diesel vehicles by means of remote sensing

To improve the accuracy of detecting high NO (nitric oxide) emissions from heavy-duty diesel vehicles (HDDV) by remote sensing (RS), the emissions of one HDDV complied with China V regulation and one HDDV complied with China VI regulation at constant speeds, with and without after-treatment devices, are tested by a portable emission measurement system (PEMS) and RS. The optimized measurement procedures for detecting high NO emissions from China V and China VI HDDVs by RS are summarized. The correlation of RS and PEMS data shows that the ratio of NO to CO2 (carbon dioxide) is a more appropriate RS measurement than NO concentration alone for identifying high emitters, although NO concentrations of 600 ppm and 100 ppm can be used as a basis for distinguishing between China V and China VI HDDVs, respectively. When the NO/CO2 ratio is >200 × 10−4 and 25 × 10−4, identifying China V and China VI HDDV high emitters, respectively, is possible. Additionally considering the vehicle speed can reduce the high emitter identification error rate, and excluding data where vehicle acceleration is less than −0.1 m/s2 can further improve identification accuracy. Four new high-emitter identification methods based on different combinations of measurements are shown to improve identification efficiency with only small increases in identification error. This study provides evidence to support the future development of high-precision RS methodologies for identifying high-emission vehicles.

A Long Time Span-Specific Emitter Identification Method Based on Unsupervised Domain Adaptation

Specific emitter identification (SEI) is a professional technology to recognize different emitters by measuring the unique features of received signals. It has been widely used in both civilian and military fields. Recently, many SEI methods based on deep learning have been proposed, most of which assume that the training set and testing set have the same data distribution. However, in reality, the testing set is generally used later than the training set and lacks labels. The long time span may change the signal transmission environment and fingerprint features. These changes result in considerable differences in data distribution between the training and testing sets, thereby affecting the recognition and prediction abilities of the model. Therefore, the existing works cannot achieve satisfactory results for a long time span SEI. To address this challenge and obtain stable fingerprints, we transform the long time span SEI problem into a domain adaptive problem and propose an unsupervised domain adaptive method called LTS-SEI. Noteworthily, LTS-SEI uses a multilayer convolutional feature extractor to learn feature knowledge and confronts a domain discriminator to generate domain-invariant shallow fingerprints. The classifier of LTS-SEI applies feature matching to source domain samples and target domain samples to achieve the domain alignment of deep fingerprints. The classifier further reduces the intraclass diversity of deep features to alleviate the misclassification problem of edge samples in the target domain. To confirm the effectiveness and reliability of LTS-SEI, we collect multiple sets of real satellite navigation signals using two antennas with 13 m- and 40 m-large apertures, respectively, and construct two available datasets. Numerous experiments demonstrate that LTS–SEI can considerably increase the recognition accuracy of the long time span SEI and is superior to the other existing methods.

Semi-Supervised Specific Emitter Identification via Dual Consistency Regularization

Deep Learning (DL)-based Specific Emitter Identification (SEI) is a potential physical layer authentication technique for Industrial Internet-of-Things (IIoT) Security, which detects the individual emitter according to its unique signal features resulting from transmitter hardware impairments. The success of DL-based SEI often depends on sufficient training samples and the integrity of samples’ labels. The extensive deployment of wireless devices generates a huge amount of signals, but signals labeling is quite difficult and expensive with the high demand for expertise. In this paper, we present a SEI method based on Dual Consistency Regularization (DCR), which enables feature extraction and identification using a few labeled samples and a large number of unlabeled samples. With the help of pseudo-labeling, we leverage consistency between the predicted class distribution of weakly-augmented unlabeled training samples and that of strongly-augmented training unlabeled samples, and consistency between semantic feature distribution of labeled samples and that of pseudo-labeled samples, which takes the unlabeled samples into account to model parameter tuning for a more accurate emitter identification. Extensive numerical results demonstrate that compared with well-known semi-supervised learning-based SEI methods, our method obtains 99.77% identification accuracy on a Wi-Fi dataset and 90.10% identification accuracy on an Automatic Dependent Surveillance-Broadcast (ADS-B) dataset when only 10% of training samples are labeled, and improves the identification accuracy on the Wi-Fi dataset and the ADS-B dataset by more than 19.07% and 5.30%, respectively. Our codes are available at https://github.com/lovelymimola/DCR-Based-SemiSEI.

An investigation into the impacts of deep learning‐based re‐sampling on specific emitter identification performance

AbstractIncreasing Internet of Things (IoT) deployments present a growing surface over which villainous actors can carry out attacks. This disturbing revelation is amplified by the fact that most IoT devices use weak or no encryption. Specific Emitter Identification (SEI) is an approach intended to address this IoT security weakness. This work provides the first Deep Learning (DL) driven SEI approach that upsamples the signals after collection to improve performance while reducing the hardware requirements of the IoT devices that collect them. DL‐driven upsampling results in superior SEI performance versus two traditional upsampling approaches and a convolutional neural network‐only approach.

Research on the Enhancement Method of Specific Emitter Open Set Recognition

Open set recognition (OSR) aims at dealing with unknown classes that are not included in the train set. However, existing OSR methods rely on deep learning networks that perform supervised learning on known classes in the train set, resulting in poor performance when the unknown class is very similar to the known class. Considering the subtle individual differences under the same type in specific emitter identification (SEI) applications, it is difficult to distinguish between known classes and unknown classes in open set scenarios. This paper proposes a pseudo signal generation and recognition neural network (PSGRNN) to address relevant problems in this situation. PSGRNN applies complex-value convolution operations to accommodate IQ signal inputs. Its key idea is to utilize samples of known classes to generate pseudo samples of unknown classes. Then, the samples of known classes and the generated pseudo samples of unknown classes are jointly input into the neural network to construct a new classification task for training. Moreover, the center loss is improved by adding inter-class penalties to maximize the inter-class difference. This helps to learn useful information for separating known and unknown classes, resulting in clearer decision boundaries between the known and the unknown. Extensive experiments on various benchmark signal datasets indicate that the proposed method achieves more accurate and robust open set classification results, with an average accuracy improvement of 4.62%.

A Specific Emitter Identification System Design for Crossing Signal Modes in the Air Traffic Control Radar Beacon System and Wireless Devices.

To improve communication stability, more wireless devices transmit multi-modal signals while operating. The term 'modal' refers to signal waveforms or signal types. This poses challenges to traditional specific emitter identification (SEI) systems, e.g., unknown modal signals require extra open-set mode identification; different modes require different radio frequency fingerprint (RFF) extractors and SEI classifiers; and it is hard to collect and label all signals. To address these issues, we propose an enhanced SEI system consisting of a universal RFF extractor, denoted as multiple synchrosqueezed wavelet transformation of energy unified (MSWTEu), and a new generative adversarial network for feature transferring (FTGAN). MSWTEu extracts uniform RFF features for different modal signals, FTGAN transfers different modal features to a recognized distribution in an unsupervised manner, and a novel training strategy is proposed to achieve emitter identification across multi-modal signals using a single clustering method. To evaluate the system, we built a hybrid dataset, which consists of multi-modal signals transmitted by various emitters, and built a complete civil air traffic control radar beacon system (ATCRBS) dataset for airplanes. The experiments show that our enhanced SEI system can resolve the SEI problems associated with crossing signal modes. It directly achieves 86% accuracy in cross-modal emitter identification using an unsupervised classifier, and simultaneously obtains 99% accuracy in open-set recognition of signal mode.