RF Transmitter Identification and Classification Based on Deep Residual Shrinkage Network

In the specific emitter identification, the current traditional classification and identification methods have difficulty in extracting fingerprint features, and the accuracy of fingerprint identification is low. In order to better extract the fingerprint features, a specific emitter identification algorithm is proposed based on the Convolutional Neural Network Dense Block module, called the Deep Dense Feature Network (DDFN). The network can effectively realize automatic extraction and multiplexing of fingerprint features, strengthen the transfer of features, and reduce the problem of vanishing gradients. The experimental results show that DDFN can achieve a classification accuracy of 93.17%, and can better realize the identification of specific radiation sources in a complex electro-magnetic environment.

Specific emitter identification (SEI), as an important problem in situational awareness, identifies emitters via unique characteristics. However, current SEI methods mostly suffer from appropriately setting the trade-off between comprehensiveness and efficiency when extracting fingerprint features. To address the issue, this paper provides a novel SEI framework with a separate representation module. Within the novel framework, manifolds are proposed to be signal representations and multi-level manifold features are extracted as fingerprint features. We first build the SEI model from the nonlinear dynamic perspective, where the SEI process identifies the nonlinear systems via a measurement sequence. Then, we demonstrate that manifolds can represent emitters equivalently and prove the one-to-one correspondence between manifolds and emitter individuals. Hence, manifolds can highlight unique nonlinear dynamic characteristics and simultaneously describe comprehensive system working processes. The coordinate delayed technique and manifold learning methods are employed to reconstruct the phase space and manifold, respectively. For accomplishing the identification task, multi-level manifold features, comprising intrinsic dimension, topological features, conformal features, and Riemannian metric features, are extracted from the reconstructed manifolds and input to an ensemble learning scheme, named Adaboost. Extensive simulation and real-world experiments agree with our analytical conclusions and confirm the proposed method’s efficiency. The results also demonstrate that the proposed method achieves a high recognition accuracy, outstanding adaptability, and strong robustness.

Data-driven rolling-bearing fault diagnosis methods are mostly based on deep-learning models, and their multilayer nonlinear mapping capability can improve the accuracy of intelligent fault diagnosis. However, problems such as gradient disappearance occur as the number of network layers increases. Moreover, directly taking the raw vibration signals of rolling bearings as the network input results in incomplete feature extraction. In order to efficiently represent the state characteristics of vibration signals in image form and improve the feature learning capability of the network, this paper proposes fault diagnosis model MTF-ResNet based on a Markov transition field and deep residual network. First, the data of raw vibration signals are augmented by using a sliding window. Then, vibration signal samples are converted into two-dimensional images by MTF, which retains the time dependence and frequency structure of time-series signals, and a deep residual neural network is established to perform feature extraction, and identify the severity and location of the bearing faults through image classification. Lastly, experiments were conducted on a bearing dataset to verify the effectiveness and superiority of the MTF-ResNet model. Features learned by the model are visualized by t-SNE, and experimental results indicate that MTF-ResNet showed better average accuracy compared with several widely used diagnostic methods.

To solve the problem that the neural network-based specific emitter identification (SEI) method is limited by the training scene and has poor generalization ability in the complex scene, an SEI method based on deep adversarial domain adaptation (DADA) is proposed. DADA integrates deep neural networks and adversarial methods into the domain adaptation problem of transfer learning. It trains the feature extractor by maximizing the domain discrimination loss and transfers the high-quality deep radio frequency fingerprint (RFF) features extracted under weak interference conditions to the unlabeled emitter of the same class affected by the channel and noise. At the same time, the loss of the sample classifier is minimized, and the deep RFF features of unlabeled emitters can be accurately classified and identified. By repeating the above-mentioned adversarial process, the network realizes unsupervised transfer learning based on deep RFF features. The analysis of the identification results of 20 CC2530 devices under the Rayleigh channel and different signal-to-noise ratio (SNR) conditions proves that DADA can effectively improve the network's identification performance for unlabeled emitters under different conditions and has good performance of adaptability and generalization.

Specific Emitter Identification (SEI) is the technique that identifies the individual radio emitter using the Radio Frequency Fingerprint (RFF), which are originated from the imperfections and differences of transmitters. Previous SEI techniques are sensitive to noise and need enough sampled points. In this paper, a novel SEI approach to extracting fingerprint features of energy envelope of transient signals is proposed. A linear system model is utilized to fit the energy envelope, and the fingerprinting features are constructed by the polynomial coefficients estimated with least-squares algorithm. The results of experiments on actual burst signals demonstrate that the method is effective.

Nowadays, much attention in electronic warfare is paid to the Specific Emitter Identification (SEI), which seems to be one of the most difficult subjects in the field. The principal function of the SEI is recognizing the different radar devices of the same type. In most cases, classical methods of identification could not be enough to realize the task. The main unique characteristics of a modern radar emitter are hidden in the structures of the time-domain waveforms, especially the pulse envelope and instantaneous frequency, which are used as the fingerprint features in this paper. In order to measure the distinctions among different radar emitters of the same type, the frechet distance which provides an effective way to calculate the distance between curves, the pulse envelopes or instantaneous frequencies, is chosen to achieve the SEI. The basic theory of the frechet distance is described and the experimental results are presented in the paper.

In recent years, the growing complexity of the electromagnetic environment has rendered traditional methods of specific emitter identification (SEI) inadequate in meeting the increasing demands for both high accuracy and generalization capability. Moreover, the high cost and confidentiality associated with emitter data have made the acquisition of large-scale labeled datasets exceedingly difficult, further intensifying the challenges in SEI tasks. To address these issues, this paper proposes a deep metric learning-based method for SEI. The approach first leverages a large-scale emitter signal dataset to train a high-accuracy, generalizable pre-trained model. Subsequently, the pre-trained model is fine-tuned in few-shot scenarios to achieve accurate emitter identification under data-scarce conditions. Experimental results indicate that the fine-tuning method, built upon the pre-trained model, demonstrates excellent SEI performance even with limited training data. Specifically, when only 10% of the training data is used, the model achieves an impressive accuracy of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{9 0 \%}$</tex>.

Internet of Things (IoT) deployments continue at an accelerated rate and are projected to reach 75 billion by 2025. Most IoT devices employ weak or no encryption at all due to constrained onboard resources, prohibitive manufacturing costs, and challenges due to implementing and managing encryption at scale. Specific Emitter Identification (SEI) is an approach intended to address the security risks associated with unencrypted or weakly encrypted IoT deployments. SEI passively exploits inherent, unintentional waveform features that are imparted during normal device operations, thus it does not require modification of the device being identified. Recently, Deep Learning (DL)-based SEI has garnered a lot of interest due to its ability to learn discriminatory features directly from an emitter's digitally sampled waveforms, thus eliminating feature engineering commonly associated with SEI. DL-based SEI uses the entirety of the digitally sampled waveform, which includes the unintentional as well as intentional waveform structure. The intentional waveform structure does not convey emitter specific features, thus it is information not used by the DL-based SEI process. In fact, this work shows that the intentional waveform structure acts as a confuser that inhibits the DL network's ability to learn the features needed to discern one emitter from another and its negative impacts worsen as the number of emitters increases. Our work shows that frequency-domain removal of the intentional waveform structure coupled with a Long Short-Term Memory (LSTM) results in superior SEI performance.

The specific emitter identification is widely used in electronic countermeasures, spectrum control, wireless network security and other civil and military fields. In response to the problems that the traditional specific emitter identification algorithm relies on a priori knowledge and has poor generalizability, and the existing specific emitter identification algorithm based on deep learning has poor feature selection and the adopted feature extraction network is not targeted, etc., the specific emitter identification algorithm based on multi-sequence feature learning is proposed. Firstly, multiple sequence features of the emitted signal of the communication radiation source are extracted, and these features are combined into multiple sequence features. Secondly, the multiple sequence fusion convolutional network is constructed to fuse and deeply extract the multiple sequence features and complete the classification of individual communication radiation sources through the classifier of neural network. The selected sequence features of this algorithm contain more and more essential RFF information, while the targeted design of the multi-sequence feature fusion learning network can effectively extract the essential RFF information. The results show that the algorithm can significantly improve the performance of SEI compared with the benchmark algorithm, with a recognition rate gain of about 17%.

Specific emitter identification technology aims to identify the signal emitted by a specific emitter from multiple signals. Existing techniques for specific emitter identification are faced with the problem that the identification performance decreases with the passage of time. In this paper, we propose a method for specific emitter identification at different time. The method treats the signals of radiation sources at different time as signals of separate domains, and resolves the influence of time on specific emitter identification by eliminating the factor of different domains. The experimental results show that the method has better performance on both data corresponding to the time involved in training and data corresponding to the time not involved in training compared with both domain-free migration and ordinary domain migration methods. At the same time, the proposed tagging flexible loss function is more efficient in the face of new time tag free data.

Aiming at the problem that the difference in fault data distribution caused by the variation of gear and bearing conditions during the operation of rotating machinery affects the accuracy of fault diagnosis, a multi-condition fault diagnosis approach based on whale optimization algorithm (WOA), variational mode decomposition (VMD), and deep residual shrinkage network (DRSN) is proposed. Firstly, WOA is employed to optimize the parameters of the decomposition level and the penalty factor value in VMD, enhancing its noise resistance. Subsequently, VMD processing yields several modal components, each characterized by a permutation entropy feature vector, ensuring optimal feature extraction. DRSN is then utilized, employing an attention mechanism to adaptively set soft threshold functions, thereby improving gear fault diagnosis accuracy under multiple operating conditions. Finally, the proposed method’s performance was validated through the collection of gear running signals under various conditions, and the fault diagnosis accuracy was used as the model evaluation index to conduct comparative experiments and ablation experiments. The experimental results indicate that the WOA-VMD feature extraction and DRSN fault diagnosis techniques outperform traditional methods in multi-condition gear fault diagnosis. This study provides a new effective approach for multi-condition diagnosis of rotating machinery.

Aiming at the lack of sufficient meteorological data for runoff prediction in areas with few data, the accuracy of the data is improved by using soft thresholding under the deep attention mechanism, and a deep residual shrinkage long-short memory network prediction model is constructed combined with the watershed characteristic data. The model is validated by using 134 watersheds with spatial correlations in the CAMELS dataset. According to the experimental findings, the model is more accurate than the LSTM model, HIV model, and mHM model in predicting runoff in regions with little data, with an accuracy of 86%.

The crucial issue in specific emitter identification (SEI) is the extraction of fingerprint features which can represent the differences among individual emitters of the same type. Considering that these emitters have the same intentional modulation on pulse, the fingerprint features originated from the unintentional modulation on pulse are extremely imperceptible and less detectable. However, existing feature extractions, either traditional handcrafted ones or deep learning based ones, have failed to ensure that their extracted features are rich in the unintentional modulation information (UMI) and not interfered by the intentional modulation information (IMI). To adequately take advantage of deep learning to address SEI, this Letter proposes a novel neural networks, named discriminative adversarial networks (DAN). By demarcating a clear boundary between IMI and UMI, DAN isolates IMI and thus reduces the burden of UMI mining during its feature extraction process. Experimental results demonstrate that DAN outperforms most methods in the literature.

FTAN: Feature Transform and Alignment Network for cross-domain specific emitter identification

Various microfossils from the early Cambrian provide crucial clues for understanding the Cambrian explosion and the origin of animal phyla. However, specimens with important anatomical structures are extremely rare and the efficiency of retrieving such fossils by traditional manual selection under a microscope is quite low. Such a contradiction has hindered breakthroughs in micropaleontology for a long time. Here, we propose a solution for identifying specific taxa of Cambrian microfossils using only a few available specimens by transferring a model pre-trained on natural image datasets to the field of paleontological artificial intelligence. The method employs a 34-layer deep residual neural network as the underlying framework, migrates the ImageNet pre-trained model, freezes the low-layer network parameters and retrains the high-layer parameters to build a microfossil image recognition model. We built training sets with randomly selected images of varied number for each taxon. Our experiments show that the average recognition accuracy for specific taxa of Cambrian microfossils (50 images for each taxon) is higher than 0.97 and it can reach 0.85 with only three training samples per taxon. Comparative analyses indicate that our results are much better than those of various prevalent methods, such as the transpose convolutional neural network (TCNN). This demonstrates the feasibility of using natural images (ImageNet) for the training of microfossil recognition models and provides a promising tool for the discovery of rare fossils.