Fine-Tuning a Deep Metric Learning Model for Few-Shot Specific Emitter Identification

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.

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.

Radar specific emitter identification (SEI) is the process of uniquely identifying an individual emitter from the same class of radars by their individual properties that arise from hardware imperfections. However, it is challenging or perhaps impossible to generate globally unique emitter identifiers by using SEI techniques alone, due to the increasing number of radars and the subtle differences in their signal properties. We therefore introduce a multitier radar emitter identification concept that includes radar function identification, model identification, and SEI. The combination of function identifiers, model identifiers, and specific emitter identifiers could generate globally unique radar emitter identifiers. In this article, we propose to use the radio frequency (RF) features extracted from time domain transient signals for radar model identification. The RF features include the duration, maximum derivative, skewness, kurtosis, mean, variance, fractal dimension, Shannon entropy, and polynomial coefficients of the normalized energy trajectory of a transient signal, as well as the area under the trajectory curve. We propose three RF fingerprints for radar model identification, each consisting of a predetermined subset of the features. The performance of the RF fingerprints was evaluated by using five classification algorithms with two radar datasets. Our results show attractive performance with respect to hetero-model radar identification. In particular, for the hetero-model radar dataset, the pair of the all-features (AF) fingerprint and gradient boosting algorithm achieved 91.9, 90.25, and 91.1% classification accuracy for three, four, and five emitters, respectively. On this basis, we conclude that the proposed AF fingerprint could be applied directly to radar model identification by training the gradient boosting algorithm using a dataset with many radars per model.

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 emerging physical-layer security authentication method, is crucial for maintaining information security in the Internet of Things. However, existing deep learning-based SEI methods require extensive labeled data for training, which are often unavailable in untrusted scenarios. Furthermore, due to the subtle nature of radio-frequency fingerprints, unsupervised SEI struggles to achieve high accuracy in identification without the guidance of labels. In this paper, we propose an unsupervised SEI method based on group label-driven contrastive learning (GLD-CL). We propose a novel method for constructing the dataset: all input samples derived from the same received signal segment are grouped together and assigned a unique identifier, termed the group label. Based on this, we improve the loss function of self-supervised contrastive learning. With the assistance of group labels, the feature vectors of the same class in the feature space become more closely clustered, enhancing the accuracy of unsupervised SEI. Extensive experimental results based on real-world datasets demonstrate that the normalized mutual information of GLD-CL achieves 96.4% accuracy, representing an improvement of 5.68% or more compared to the baseline algorithms. Furthermore, GLD-CL exhibits robust performance, achieving good identification accuracy across various signal-to-noise ratio scenarios.

Traditional specific emitter identification (SEI) methods need to artificially design extraction methods of the signal fingerprint features, which require a large amount of calculation and low classification accuracy. A supervised SEI framework based on deep residual shrinkage Network (DRSN) is proposed in this paper, which is used to adaptively learn the deep features of transmitted signals and classify them. In our framework, soft thresholding is introduced to reduce the noise of the original I/Q signal which contains a lot of noise, so as to reduce the influence of noise on the inaccuracy of RF fingerprint feature extraction. The I/Q signal of the receiver is sliced and spliced (pre-processing step) before inputting to the DRSN model to reduce the computational complexity. The experimental results show that the proposed scheme can correctly distinguish 16 credible transmitters with 98% accuracy respectively. Compared with the existing SEI methods, our scheme has the advantages of excellent identification accuracy and strong anti-interference ability.

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.

The process of final emitter identification is based on ldquothe knowledge-based approachrdquo which was implemented during the processing of constructing the database. The technical and tactical parameters connected with radarpsilas applications are very often a good source of knowledge about the identified emitters. The paper discusses the role of knowledge-based processing methods and how they may be applied to the signal processing functions of de-interleaving, merge and specific emitter identification. A few examples of intercepted signals from radars of different applications are presented.

Deep learning has brought great development to radar emitter identification technology. In addition, specific emitter identification (SEI), as a branch of radar emitter identification, has also benefited from it. However, the complexity of most deep learning algorithms makes it difficult to adapt to the requirements of the low power consumption and high-performance processing of SEI on embedded devices, so this article proposes solutions from the aspects of software and hardware. From the software side, we design a Transformer variant network, lightweight convolutional Transformer (LW-CT) that supports parameter sharing. Then, we cascade convolutional neural networks (CNNs) and the LW-CT to construct a one-dimensional-CNN-Transformer(1D-CNN-Transformer) lightweight neural network model that can capture the long-range dependencies of radar emitter signals and extract signal spatial domain features meanwhile. In terms of hardware, we design a low-power neural network accelerator based on an FPGA to complete the real-time recognition of radar emitter signals. The accelerator not only designs high-efficiency computing engines for the network, but also devises a reconfigurable buffer called “Ping-pong CBUF” and two-level pipeline architecture for the convolution layer for alleviating the bottleneck caused by the off-chip storage access bandwidth. Experimental results show that the algorithm can achieve a high recognition performance of SEI with a low calculation overhead. In addition, the hardware acceleration platform not only perfectly meets the requirements of the radar emitter recognition system for low power consumption and high-performance processing, but also outperforms the accelerators in other papers in terms of the energy efficiency ratio of Transformer layer processing.

Specific emitter identification (SEI) and automatic modulation classification (AMC) aregenerally two separate tasks in the field of radio monitoring. Both tasks have similarities in terms oftheir application scenarios, signal modeling, feature engineering, and classifier design. It is feasibleand promising to integrate these two tasks, with the benefit of reducing the overall computationalcomplexity and improving the classification accuracy of each task. In this paper, we propose adual-task neural network named AMSCN that simultaneously classifies the modulation and thetransmitter of the received signal. In the AMSCN, we first use a combination of DenseNet andTransformer as the backbone network to extract the distinguishable features; then, we design amask-based dual-head classifier (MDHC) to reinforce the joint learning of the two tasks. To trainthe AMSCN, a multitask cross-entropy loss is proposed, which is the sum of the cross-entropyloss of the AMC and the cross-entropy loss of the SEI. Experimental results show that our methodachieves performance gains for the SEI task with the aid of additional information from the AMCtask. Compared with the traditional single-task model, our classification accuracy of the AMC isgenerally consistent with the state-of-the-art performance, while the classification accuracy of the SEIis improved from 52.2% to 54.7%, which demonstrates the effectiveness of the AMSCN.

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 (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.

Machine learning becomes increasingly promising in specific emitter identification (SEI), particularly in feature extraction and target recognition. Traditional features, such as radio frequency (RF), pulse amplitude (PA), power spectral density (PSD), and etc., usually show limited recognition effects when only a slight difference exists in radar signals. Numerous two-dimensional features on transform domain, like various time-frequency representation and ambiguity function are used to augment information abundance, whereas the unacceptable computational burden usually emerges. To solve this problem, some artfully handcrafted features in transformed domain are proposed, like representative slice of ambiguity function (AF-RS) and compressed sensing mask (CS-MASK), to extract representative information that contributes to machine recognition task. However, most handcrafted features only utilizing neural network as a classifier, few of them focus on mining deep informative features from the perspective of machine cognition. Such feature extraction that is based on human cognition instead of machine cognition may probably miss some seemingly nominal texture information which actually contributes greatly to recognition, or collect too much redundant information. In this paper, a novel data-driven feature extraction is proposed based on machine cognition (MC-Feature) resort to saliency detection. Saliency detection exhibits positive contributions and suppresses irrelevant contributions in a transform domain with the help of a saliency map calculated from the accumulated gradients of each neuron to input data. Finally, positive and irrelevant contributions in the saliency map are merged into a new feature. Numerous experimental results demonstrate that the MC-feature can greatly strengthen the slight intra-class difference in SEI and provides a possibility of interpretation of CNN.

Specific Emitter Identification (SEI) is the approach to identify emitter individuals using received wireless signals. Despite the fact that deep learning has been successfully applied in SEI, the performance is still unsatisfying when the receiver changes. In this paper, we introduce a domain adaptation method, namely Deep Adversarial Neural Network (DANN), for cross-receiver SEI. Furthermore, separated batch normalization (SepBN) is proposed to improve the performance. Results of experiments using real data show that the proposed SepBN-DANN method performs desirably for cross-receiver SEI.

Specific emitter identification (SEI) is a non-cryptographic authentication technique to provide an extra security layer for wireless devices, which has promising applications. However, the traditional methods of SEI are only available in limited equipments. In actual application scenarios, new devices (as new classes) are constantly appearing. In this paper, an effective class incremental learning (CIL) method is proposed for SEI, named class-incremental with self-training and prototype augmentation (CISP). It is a teacher-student network. Firstly, the teacher network trained by the old-class data is utilized to instruct the student network to adapt the new classes while retaining the old-class knowledge through the knowledge distillation (KD) techniques. Secondly, in order to mitigate the problem of favoring the new classes, weight aligning (WA) method is introduced to balance the weights of the new-class and old-class classification layers in the student network. Lastly, the old-class samples are recalled from the unlabeled dataset by the student network and input into the teacher network. Then the feature prototypes of the old classes are constructed and augmented. This would further ease the imbalance between the old and new classes and alleviate the problem of noisy pseudo-labels. Experiment results on the real AIS-100 dataset and ADS-B-100 dataset with the number of the initial classes being 20 and 20 classes per incremental step demonstrate that the proposed method can achieve an average accuracy of 95.29% and 95.84%, respectively. It effectively mitigates the catastrophic forgetting of the model and is superior to the state-of-the-art incremental learning approaches of not saving the old-class samples.