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Radar radiation source recognition is critical for the reliable operation of radar communication systems. However, in increasingly complex electromagnetic environments, traditional identification methods face significant limitations. These methods often struggle with high noise levels and diverse modulation types, making it difficult to maintain accuracy, especially when the Signal-to-Noise Ratio (SNR) is low or the available training data are limited. These difficulties are further intensified by the necessity to generalize in environments characterized by a substantial quantity of noisy, low-quality signal samples while being constrained by a limited number of desirable high-quality training samples. To more effectively address these issues, this paper proposes a novel approach utilizing Model-Agnostic Meta-Learning (MAML) to enhance model adaptability in few-shot learning scenarios, allowing the model to quickly learn with limited data and optimize parameters effectively. Furthermore, a multimodal fusion neural network, DCFANet, is designed, incorporating residual blocks, squeeze and excitation blocks, and a multi-scale CNN, to fuse I/Q waveform data and time–frequency image data for more comprehensive feature extraction. Our model enables more robust signal recognition, even when the signal quality is severely degraded by noise or when only a few examples of a signal type are available. Testing on 13 intra-pulse modulated signals in an Additive White Gaussian Noise (AWGN) environment across SNRs ranging from −20 to 10 dB demonstrated the approach's effectiveness. Particularly, under a 5 − w a y 5 − s h o t setting, the model achieves high classification accuracy even at −10dB SNR. Our research underscores the model's ability to address the key challenges of radar emitter signal recognition in low-SNR and data-scarce conditions, demonstrating its strong adaptability and effectiveness in complex, real-world electromagnetic environments. [ABSTRACT FROM AUTHOR]

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In recent years, the rapid development of deep learning technology has brought new opportunities for specific emitter identification and has greatly improved the performance of radar emitter identification. The most specific emitter identification methods, based on deep learning, have focused more on studying network structures and data preprocessing. However, the data selection and utilization have a significant impact on the emitter recognition efficiency, and the method to adaptively determine the two parameters by a specific recognition model has yet to be studied. This paper proposes a knowledge graph-driven convolutional neural network (KG-1D-CNN) to solve this problem. The relationship network between radar data is modeled via the knowledge graph and uses 1D-CNN as the metric kernel to measure these relationships in the knowledge graph construction process. In the recognition process, a precise dataset is constructed based on the knowledge graph according to the task requirement. The network is designed to recognize target emitter individuals from easy to difficult by the precise dataset. In the experiments, most algorithms achieved good recognition results in the high SNR case (10–15 dB), while only the proposed method could achieve more than a 90% recognition rate in the low SNR case (0–5 dB). The experimental results demonstrate the efficacy of the proposed method. [ABSTRACT FROM AUTHOR]

Radar emitter signal deinterleaving is a key technology for radar signal reconnaissance and an essential part of battlefield situational awareness. This paper systematically sorts out the mainstream technology of radar emitter signal deinterleaving. It summarizes the main research progress in radar emitter signal deinterleaving from three directions: interpulse modulation characteristics-based, intrapulse modulation characteristics-based, and machine learning-based research. Particularly, this paper focuses on explaining the principle and technical characteristics of the latest deinterleaving technology, such as neural network-based and data stream clustering-based techniques. Finally, the shortcomings of the current radar emitter deinterleaving technology are summarized, and the future trend is predicted.

In order to solve the problem of low accuracy of radar emitter individual identification in complex electromagnetic environment, a radar emitter individual identification technology based on parameter optimization VMD and LightGBM is proposed. Firstly, the unintentional features of the radar emitter are analyzed, and the added phase noise is taken as the fingerprint feature of radar emitter in the simulation. Secondly, sparrow search algorithm (SSA) is used to automatically optimize the decomposition parameters of variational modal decomposition (VMD), and the optimal decomposition parameter combination is accurately and quickly obtained as [2, 2 950]. Then, based on the optimal VMD decomposition parameters, the energy entropy and sample entropy of the emitter signal are extracted as feature vector. Finally, the feature vector is sent to the LightGBM classifier to complete the emitter individual identification. Through the verification of measured data, the recognition rate can reach more than 85% when the signal-to-noise ratio is 25 dB, which has ideal recognition results.

Radar pulse deinterleaving is the core part of electronic reconnaissance system, which is responsible for sorting out the pulses coming from different emitters. In the process of signal deinterleaving, parameter clustering is an important method. The features for clustering are mainly derived from pulse parameters emitted by the radiation source transmitter. When the multiple target signals are seriously mixed in a complex environment, the clustering accuracy decreases. To solve this problem, a distributed clustering method based on spatial information is proposed. First, a multi-node distributed system is built, one of the nodes is taken as the central node, and the pulse information transmitted by other nodes through the link is received. All pulses at the central node are jointly clustered according to the three parameters of radio frequency (RF), pulse width (PW) and time of arrival (TOA), forming an associated pulse description word (APDW). Then, the associated pulse is spatially marked based on its spatial information within the range of the target area of interest. Finally, the associated pulse is re-clustered using the marked information and pulse parameters to achieve effective target separation. The simulation results show that the method can overcome the influence of parameter agility and make full use of the spatial information of the target to improve the accuracy of pulse clustering.

In the traditional radar emitter recognition task, the number of emitter category tags is fixed and relatively small. We input the pulse characteristic parameters into the machine learning model to model the radar emitter. The radar emitters with agile waveform involves four different waveform categories. The number of specific emitter categories contained in different waveform categories is extremely unbalanced. Their parameters vary widely, so only using the same model or strategy to model each waveform category cannot effectively extract the core features of the radar emitters with agile waveform. The above is the main reason for the low recognition accuracy of the recognition task of radar emitters with agile waveform. In this paper, a deep reinforcement learning framework is designed to be used in the intelligent recognition task of radar emitters with agile waveform. The task is regarded as a two‐step decision game. We use CNN and Bi‐LSTM to model the radar emitter, and calculate the initial state and transition state of the game. At the same time, we design the penalty function in reinforcement learning and increase the penalty for the wrong decision in the first step to deal with the imbalance of the number of emitters between different waveform categories. Finally, the Q‐Learning algorithm with approximate values is used to learn the control strategy of the game, that is, the modeling and recognition strategy adopted for emitters of different waveform categories. The simulation experiment results show that the deep reinforcement learning framework constructed in the article can improve the recognition accuracy of 1.2% compared with the state‐of‐the‐art method in the intelligent recognition task of radar emitters with agile waveform. [ABSTRACT FROM AUTHOR]

With the increasing complexity of the electromagnetic environment and the continuous development of radar technology, more and more modern digital programmable radars using agile waveform will appear in the future battlefield. It is difficult to effectively identify these radar emitters with complex system only by relying on traditional recognition models. In response to the above problem, this paper proposes a recognition method of radar emitters with agile waveform based on hybrid deep neural network and attention mechanism to deal with the problem of variable conventional characteristic parameters of radar emitter signals with agile waveform. First, we perform a distributed representation of the pulse signal data to generate high-dimensional sparse signal features. Then we design to use a dynamic Convolutional Neural Network to extract features of structural details of radar emitter signals with agile waveform at different levels, and use a Long Short-Term Memory to extract its timing features. In order to obtain the deep features that can characterize the agility of the waveform, the attention mechanism-based method is used to fuse the extracted structural features and timing features, and at the same time it can reduce the influence of noise in complex electromagnetic environment on the characteristic data of radar emitter. Finally, the deep feature is input into the Softmax layer to complete the recognition of radar emitters with agile waveform. The experimental results show that the method proposed in this paper can effectively solve the problem of the recognition of radar emitters with agile waveform, and the recognition accuracy is improved by 1.26% compared with the traditional models and other deep models.

In recent years, the rapid development of deep learning technology has brought new opportunities for specific emitter identification and has greatly improved the performance of radar emitter identification. The most specific emitter identification methods, based on deep learning, have focused more on studying network structures and data preprocessing. However, the data selection and utilization have a significant impact on the emitter recognition efficiency, and the method to adaptively determine the two parameters by a specific recognition model has yet to be studied. This paper proposes a knowledge graph-driven convolutional neural network (KG-1D-CNN) to solve this problem. The relationship network between radar data is modeled via the knowledge graph and uses 1D-CNN as the metric kernel to measure these relationships in the knowledge graph construction process. In the recognition process, a precise dataset is constructed based on the knowledge graph according to the task requirement. The network is designed to recognize target emitter individuals from easy to difficult by the precise dataset. In the experiments, most algorithms achieved good recognition results in the high SNR case (10–15 dB), while only the proposed method could achieve more than a 90% recognition rate in the low SNR case (0–5 dB). The experimental results demonstrate the efficacy of the proposed method.

With the increasing complexity of the electromagnetic environment and the continuous development of radar technology, more and more modern digital programmable radars using agile waveform will appear in the future battlefield. It is difficult to effectively identify these radar emitters with complex system only by relying on traditional recognition models. In response to the above problem, this paper proposes a recognition method of radar emitters with agile waveform based on hybrid deep neural network and attention mechanism to deal with the problem of variable conventional characteristic parameters of radar emitter signals with agile waveform. First, we perform a distributed representation of the pulse signal data to generate high-dimensional sparse signal features. Then we design to use a dynamic Convolutional Neural Network to extract features of structural details of radar emitter signals with agile waveform at different levels, and use a Long Short-Term Memory to extract its timing features. In order to obtain the deep features that can characterize the agility of the waveform, the attention mechanismbased method is used to fuse the extracted structural features and timing features, and at the same time it can reduce the influence of noise in complex electromagnetic environment on the characteristic data of radar emitter. Finally, the deep feature is input into the Softmax layer to complete the recognition of radar emitters with agile waveform. The experimental results show that the method proposed in this paper can effectively solve the problem of the recognition of radar emitters with agile waveform, and the recognition accuracy is improved by 1.26% compared with the traditional models and other deep models. [ABSTRACT FROM AUTHOR]

In order to solve the problem of high time complexity in the signal recognition process by using convolution neural network, this paper proposed a new algorithm based on denoising auto-encoder and convolution neural network. Firstly, it performed the STFT on the radar emitter signal to obtain the time frequency image. And it used gray and threshold. It put processed image vectorization into the denoising auto-encoder. Then the denoising auto-encoder extracted the feature data. It put the reconstituted image matrix into the convolution neural network and used softmax classifier as classification and recognition. The simulation shows that the model using DAE is better than the original model and the time complexity decreases greatly. The recognition rate can reach 80% under SNR = - 6 dB. Besides, compared with the performance of traditional way, the recognition effect is obviously improved. [ABSTRACT FROM AUTHOR]

At present, there are two main problems in the commonly used radar emitter identification methods. First, when the distribution of training data and testing data is quite different, the identification accuracy is low. Second, the traditional identification methods usually include an offline training stage and online identifying stage, which cannot achieve the real-time identification of the radar emitter. Aimed at the above problems, this paper proposes a radar emitter identification method based on transfer learning and online learning. First, for the case where the target domain contains only a small number of labeled samples, the TrAdaBoost method is used as the basic learning framework to train a support vector machine, which can obtain useful knowledge from the source domain to aid in the identification of the target domain. Then, for the case where the target domain does not contain labeled samples, the Expectation-Maximization algorithm is used to filter the unlabeled samples in the target domain to generate the available training data. Finally, to make the identification quickly and accurately, we propose a radar emitter identification method, based on online learning to ensure real-time updating of the model. Simulation experiments show that the proposed method, based on transfer learning and online learning, has higher identification accuracy and good timeliness.

In this study, the authors focus on pulse modulation waveform estimation based on a sequence of intercepted pulses from the same radar emitter. In general, modulation waveform estimators first perform pulse alignment in time and frequency separately, and then accumulate the aligned pulses to estimate the waveform. However, commonly used alignment methods may lead to considerable alignment errors which are difficult to detect and compensate, especially under low signal‐to‐noise ratio (SNR) conditions, resulting in non‐ideal effects of accumulation. This study proposes a robust and nearly optimal modulation waveform estimation algorithm. The new algorithm first aligns pulses in time and frequency jointly via the cross‐ambiguity function to avoid the transfer and accumulation of alignment errors. After that, an iterative maximum‐likelihood estimator is invoked to achieve the waveform estimation. Theoretical analysis and extensive experiments show that the proposed algorithm has much smaller alignment errors and better modulation waveform and modulation parameter estimation ability than competing methods at low SNRs, and can approach the ideal case. Moreover, this algorithm does not make any assumption on the type of modulation and is computationally efficient, thus having broad applications. [ABSTRACT FROM AUTHOR]

In order to solve the problems that radar emitter signal recognition evaluation methods are messy and evaluation indexes are confusing, a novel radar emitter signal recognition evaluation method based on I TOPSIS is put forward. There are three improving points compared with the traditional TOPSIS method. That is using the entropy to build index weights, using theoretical extreme value to determine the ideal point and negative ideal point, using interval distance to compute the distance between evaluation plan and ideal and negative ideal points. Then use the I TOPSIS method in effectiveness evaluation of radar emitter signal recognition. At last a comparing simulation between the novel method and traditional TOPSIS method is done, whose results prove that the novel method is more reasonable and feasible. [ABSTRACT FROM AUTHOR]

At present, there are two main problems in the commonly used radar emitter identification methods. First, when the distribution of training data and testing data is quite different, the identification accuracy is low. Second, the traditional identification methods usually include an offline training stage and online identifying stage, which cannot achieve the real-time identification of the radar emitter. Aimed at the above problems, this paper proposes a radar emitter identification method based on transfer learning and online learning. First, for the case where the target domain contains only a small number of labeled samples, the TrAdaBoost method is used as the basic learning framework to train a support vector machine, which can obtain useful knowledge from the source domain to aid in the identification of the target domain. Then, for the case where the target domain does not contain labeled samples, the Expectation-Maximization algorithm is used to filter the unlabeled samples in the target domain to generate the available training data. Finally, to make the identification quickly and accurately, we propose a radar emitter identification method, based on online learning to ensure real-time updating of the model. Simulation experiments show that the proposed method, based on transfer learning and online learning, has higher identification accuracy and good timeliness. [ABSTRACT FROM AUTHOR]

Aiming at the current problem that it is difficult to deal with an unknown radar emitter in the radar emitter identification process, we propose an unknown radar emitter identification method based on semi-supervised and transfer learning. Firstly, we construct the support vector machine (SVM) model based on transfer learning, using the information of labeled samples in the source domain to train in the target domain, which can solve the problem that the training data and the testing data do not satisfy the same-distribution hypothesis. Then, we design a semi-supervised co-training algorithm using the information of unlabeled samples to enhance the training effect, which can solve the problem that insufficient labeled data results in inadequate training of the classifier. Finally, we combine the transfer learning method with the semi-supervised learning method for the unknown radar emitter identification task. Simulation experiments show that the proposed method can effectively identify an unknown radar emitter and still maintain high identification accuracy within a certain measurement error range. [ABSTRACT FROM AUTHOR]