Radar Emitter Signal Recognition Research Articles

1D-CNN-Transformer for Radar Emitter Identification and Implemented on FPGA

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.

Efficient FPGA Implementation of Convolutional Neural Networks and Long Short-Term Memory for Radar Emitter Signal Recognition

In recent years, radar emitter signal recognition has enjoyed a wide range of applications in electronic support measure systems and communication security. More and more deep learning algorithms have been used to improve the recognition accuracy of radar emitter signals. However, complex deep learning algorithms and data preprocessing operations have a huge demand for computing power, which cannot meet the requirements of low power consumption and high real-time processing scenarios. Therefore, many research works have remained in the experimental stage and cannot be actually implemented. To tackle this problem, this paper proposes a resource reuse computing acceleration platform based on field programmable gate arrays (FPGA), and implements a one-dimensional (1D) convolutional neural network (CNN) and long short-term memory (LSTM) neural network (NN) model for radar emitter signal recognition, directly targeting the intermediate frequency (IF) data of radar emitter signal for classification and recognition. The implementation of the 1D-CNN-LSTM neural network on FPGA is realized by multiplexing the same systolic array to accomplish the parallel acceleration of 1D convolution and matrix vector multiplication operations. We implemented our network on Xilinx XCKU040 to evaluate the effectiveness of our proposed solution. Our experiments show that the system can achieve 7.34 giga operations per second (GOPS) data throughput with only 5.022 W power consumption when the radar emitter signal recognition rate is 96.53%, which greatly improves the energy efficiency ratio and real-time performance of the radar emitter recognition system.

Radar emitter signal recognition method based on improved collaborative semi-supervised learning

Radar emitter signal recognition method based on improved collaborative semi-supervised learning

Radar Spectrum Image Classification Based on Deep Learning

With the continuous development and progress of science and technology, the increasingly complex electromagnetic environment and the research and development of new radar systems have led to the emergence of various radar signals. Traditional methods of radar emitter identification cannot meet the needs of current practical applications. For the purpose of classification and recognition of radar emitter signals, this paper proposes an improved EfficientNetv2-s classification method based on deep learning for more precise classification and recognition of radar radiation source signals. Using 16 different types of radar signal parameters from the signal parameter setting table, the proposed method generates random data sets consisting of spectrum images with varying amplitude. The proposed method replaces two-dimensional convolution in EfficientNetV2 with one-dimensional convolution. Additionally, the channel attention mechanism of the EfficientNetv2-s is optimized and modified to obtain attention weights without dimensional reduction, resulting in superior accuracy. Compared with other deep-learning image-classification methods, the test results of this method have better classification accuracy on the test set: the top1 accuracy reaches 98.12%, which is 0.17~3.12% higher than other methods. Furthermore, the proposed method has lower complexity compared to most methods.

Few-Shot Radar Emitter Signal Recognition Based on Attention-Balanced Prototypical Network

In recent years, radar emitter signal identification has been greatly developed via the utilization of deep learning and has achieved significant improvements in identification accuracy. However, with the continuous emergence of complex regime radars and the increasing complexity of the electromagnetic environment, some new kinds of radar emitter signals collected are not in sufficient quantities to satisfy the demand of deep learning. As a result, in this paper, we adopted the prototypical network (PN) belonging to metric-based meta-learning to realize few-shot radar emitter signal recognition with the aim of meeting the needs of modern electronic warfare. Additionally, considering the problems that may arise in the field of few-shot radar emitter signal recognition, such as discriminative location bias caused by a small number of base classes or the large difference between base classes and novel classes, we proposed an attention-balanced strategy to improve meta-learning. Specifically, each channel in the feature map is forced to make the same contribution in the distinguishment of different classes. In addition, for PN, taking into account that the feature vectors of each support sample in the class are different, we set a network to exploit the relation between each support sample in the same classes, and weighted each feature vector of the support samples according to the relation. Large quantities of experiments indicate that our algorithm possesses more advantages than other algorithms.

Embedding Soft Thresholding Function into Deep Learning Models for Noisy Radar Emitter Signal Recognition

Radar emitter signal recognition under noisy background is one of the focus areas in research on radar signal processing. In this study, the soft thresholding function is embedded into deep learning network models as a novel nonlinear activation function, achieving advanced radar emitter signal recognition results. Specifically, an embedded sub-network is used to learn the threshold of soft thresholding function according to the input feature, which results in each input feature having its own independent nonlinear activation function. Compared with conventional activation functions, the soft thresholding function is characterized by flexible nonlinear conversion and the ability to obtain more discriminative features. By this way, the noise features can be flexibly filtered while retaining signal features, thus improving recognition accuracy. Under the condition of Gaussian and Laplacian noise with signal-to-noise ratio of −8 dB to −2 dB, experimental results show that the overall average accuracy of soft thresholding function reached 88.55%, which was 11.82%, 8.12%, 2.16%, and 1.46% higher than those of Sigmoid, PReLU, ReLU, ELU, and SELU, respectively.

Few-Shot Learning for Radar Emitter Signal Recognition Based on Improved Prototypical Network

In recent years, deep learning has been widely used in radar emitter signal identification and has significantly increased recognition rates. However, with the emergence of new institutional radars and an increasingly complex electromagnetic environment, the collection of high-quality signals becomes difficult, leading to a result that the amount of some signal types we own are too few to converge a deep neural network. Moreover, in radar emitter signal identification, most existing networks ignore the signal recognition of unknown classes, which is of vital importance for radar emitter signal identification. To solve these two problems, an improved prototypical network (IPN) belonging to metric-based meta-learning is proposed. Firstly, a reparameterization VGG (RepVGG) net is used to replace the original structure that severely limits the model performance. Secondly, we added a feature adjustment operation to prevent some extreme or unimportant samples from affecting the prototypes. Thirdly, open-set recognition is realized by setting a threshold in the metric module.

Radar Emitter Signal Recognition Based on Convolutional Bidirectional Long- and Short-Term Memory Network

Radar Emitter Signal Recognition Based on Convolutional Bidirectional Long- and Short-Term Memory Network

Recognition of Noisy Radar Emitter Signals Using a One-Dimensional Deep Residual Shrinkage Network.

Signal features can be obscured in noisy environments, resulting in low accuracy of radar emitter signal recognition based on traditional methods. To improve the ability of learning features from noisy signals, a new radar emitter signal recognition method based on one-dimensional (1D) deep residual shrinkage network (DRSN) is proposed, which offers the following advantages: (i) Unimportant features are eliminated using the soft thresholding function, and the thresholds are automatically set based on the attention mechanism; (ii) without any professional knowledge of signal processing or dimension conversion of data, the 1D DRSN can automatically learn the features characterizing the signal directly from the 1D data and achieve a high recognition rate for noisy signals. The effectiveness of the 1D DRSN was experimentally verified under different types of noise. In addition, comparison with other deep learning methods revealed the superior performance of the DRSN. Last, the mechanism of eliminating redundant features using the soft thresholding function was analyzed.

Radar Emitter Signal Recognition Based on One-Dimensional Convolutional Neural Network with Attention Mechanism

As the real electromagnetic environment grows complex and the quantity of radar signals turns massive, traditional methods, which require a large amount of prior knowledge, are time-consuming and ineffective for radar emitter signal recognition. In recent years, convolutional neural network (CNN) has shown its superiority in recognition so that experts have applied it in radar signal recognition. However, in the field of radar emitter signal recognition, the data are usually one-dimensional (1-D), which takes more time and storage space than by using the original two-dimensional CNN model directly. Moreover, the features extracted from convolutional layers are redundant so that the recognition accuracy is low. In order to solve these problems, this paper proposes a novel one-dimensional convolutional neural network with an attention mechanism (CNN-1D-AM) to extract more discriminative features and recognize the radar emitter signals. In this method, features of the given 1-D signal sequences are extracted directly by the 1-D convolutional layers and are weighted in accordance with their importance to recognition by the attention unit. The experiments based on seven different radar emitter signals indicate that the proposed CNN-1D-AM has the advantages of high accuracy and superior performance in radar emitter signal recognition.

Research on radar signal recognition based on automatic machine learning

With the advancement of machine learning and radar technology, machine learning is becoming more and more widely used in the field of radar. Radar scanning, signal acquisition and processing, one-dimensional range image, radar SAR, ISAR image recognition, radar tracking and guidance are all integrated into machine learning technology, but machine learning technology relies heavily on human machine learning experts for radar signal recognition. In order to realize the automation of radar signal recognition by machine learning, this paper proposes an automatic machine learning AUTO-SKLEARN system and applies it to radar radiation source signals. Identification: Firstly, this paper briefly introduces the classification of traditional machine learning algorithms and the types of algorithms specifically included in each type of algorithm. On this basis, the machine learning Bayesian algorithm is introduced. Secondly, the automatic machine learning AUTO based on Bayesian algorithm is proposed. -SKLEARN system, elaborates the process of AUTO-SKLEARN system in solving automatic selection algorithm and hyperparameter optimization, including meta-learning and its program implementation and automatic model integration construction. Finally, this paper introduces the process of automatic machine learning applied to radar emitter signal recognition. Through data simulation and experiment, the effect of traditional machine learning k-means algorithm and automatic machine learning AUTO-SKLEARN system in radar signal recognition is compared, which shows that automatic machine learning is feasible for radar signal recognition. The automatic machine learning AUTO-SKLEARN system can significantly improve the accuracy of the radar emitter signal recognition process, and the scheme is more reliable in signal recognition stability.

Analysis of Radar Emitter Signal Sorting and Recognition Model Structure

Aiming at the problem that there are some defects and shortcomings of traditional radar emitter signal sorting model structure, the structure of radar signal sorting model based on intra-pulse characteristic parameters is analyzed. Although the model is more complete than the early recognition model, there are still some shortcomings in the face of the increasingly complex electromagnetic environment and the emergence of complex radar signal sources. This article has studied this.

Robust Radar Emitter Recognition Based on the Three-Dimensional Distribution Feature and Transfer Learning.

Due to the increasing complexity of electromagnetic signals, there exists a significant challenge for radar emitter signal recognition. To address this challenge, multi-component radar emitter recognition under a complicated noise environment is studied in this paper. A novel radar emitter recognition approach based on the three-dimensional distribution feature and transfer learning is proposed. The cubic feature for the time-frequency-energy distribution is proposed to describe the intra-pulse modulation information of radar emitters. Furthermore, the feature is reconstructed by using transfer learning in order to obtain the robust feature against signal noise rate (SNR) variation. Last, but not the least, the relevance vector machine is used to classify radar emitter signals. Simulations demonstrate that the approach proposed in this paper has better performances in accuracy and robustness than existing approaches.

Recognition of radar emitter signals based on SVD and AF main ridge slice

Recognition of radar emitter signals is one of core elements in radar reconnaissance systems. A novel method based on singular value decomposition (SVD) and the main ridge slice of ambiguity function (AF) is presented for attaining a higher correct recognition rate of radar emitter signals in case of low signal-to-noise ratio. This method calculates the AF of the sorted signal and ascertains the main ridge slice envelope. To improve the recognition performance, SVD is employed to eliminate the influence of noise on the main ridge slice envelope. The rotation angle and symmetric Holder coefficients of the main ridge slice envelope are extracted as the elements of the feature vector. And kernel fuzzy c-means clustering is adopted to analyze the feature vector and classify different types of radar signals. Simulation results indicate that the feature vector extracted by the proposed method has satisfactory aggregation within class, separability between classes, and stability. Compared to existing methods, the proposed feature recognition method can achieve a higher correct recognition rate.

Radar emitter signal recognition based on multi-scale wavelet entropy and feature weighting

In modern electromagnetic environment, radar emitter signal recognition is an important research topic. On the basis of multi-resolution wavelet analysis, an adaptive radar emitter signal recognition method based on multi-scale wavelet entropy feature extraction and feature weighting was proposed. With the only priori knowledge of signal to noise ratio (SNR), the method of extracting multi-scale wavelet entropy features of wavelet coefficients from different received signals were combined with calculating uneven weight factor and stability weight factor of the extracted multi-dimensional characteristics. Radar emitter signals of different modulation types and different parameters modulated were recognized through feature weighting and feature fusion. Theoretical analysis and simulation results show that the presented algorithm has a high recognition rate. Additionally, when the SNR is greater than -4 dB, the correct recognition rate is higher than 93%. Hence, the proposed algorithm has great application value.

A New Recognition Method of Radar Emitter Signal

According to the problem that the existing radar signal recognition method cannot effectively identify the radar signal, a new recognition method based on kernel density estimation is proposed. First using kernel density estimation gets the probability density curve of radar emitter signal parameters, then storing the cures into database as the characteristics, in the end a radar emitter signal recognition algorithm based on template matching is proposed.

Modeling and Analysis of LPI Radar Signal

Radar Emitter Signal recognition is one of the key procedures of signal processing in Electronic Warfare (EW) Receiver. As there is an intense growth in the activities of modern Electronic Warfare, advanced radars are increasing and becoming main component of radars gradually. These modern radars called Low Probability of Intercept (LPI), use different complex waveforms to mask their presence from the enemy while accomplishing their mission. These waveforms cannot be detected by the use of traditional recognition algorithms and methods thus posing a major challenge to the Electronic Warfare designer. This paper presents simulation of different types of LPI signals, generation of those signals and development of Time Frequency Algorithm to analyze those signals.

A method for radar emitter signal recognition based on time-frequency atom features

A novel method for radar emitter signal recognition based on time-frequency atom feature is presented.During training,based on the over-complete time-frequency atom dictionary,a few atoms which can separate different kinds of signals best are extracted as a set of fixed feature according to the class separability.During testing,the module of inner product between atoms and signals is used as the input feature for the fuzzy ARTMAP classifier,and the radar emitter signals can be recognized automatically.Experimental results of five kinds of typical radar emitter signals show that this method reduces the computational amount of feature extraction during testing obviously,and the input features have strong concentration within classes and large separability between classes.Our method can achieve high recognition accuracy at the SNR larger than 3 dB.

Radar Emitter Signal Recognition Based on EMD and Neural Network

Radar emitter signal (RES) recognition is the important content in radar reconnaissance and signal processing. In order to study the problem of RES recognition, and to improve the RES recognition rate of the electronic warfare equipment, the empirical mode decomposition (EMD) theory and wavelet packet (WP) are introduced into RES feature extraction. A new RES recognition method is proposed based on WP, EMD and neural network (NN). It uses wavelet packet to finish decomposition, de-noising and reconstruction of the RES. Then obtain the intrinsic mode function (IMF) through EMD, which can embody the characteristics of the RES. The energy of each IMF are calculated and normalized, which would be regarded as the feature vector. By constructing back propagation neural network (BPNN) classifier and redial basis function neural network (RBFNN) classifier, it realizes the RES recognition finally. Experiment results show that the RES recognition method based on WP, EMD and NN is an effective recognition method, which can achieve satisfying correct recognition rate in a larger signal to noise ratio, and has certain reference value in follow-up in-depth study.

Novel Quantum Genetic Algorithm and Its Applications

By introducing strong parallelism of quantum computing into evolutionary algorithm, a novel quantum genetic algorithm (NQGA) is proposed. In NQGA, a novel approach for updating the rotation angles of quantum logic gates and a strategy for enhancing search capability and avoiding premature convergence are adopted. Several typical complex continuous functions are chosen to test the performance of NQGA. Also, NQGA is applied in selecting the best feature subset from a large number of features in radar emitter signal recognition. The testing and experimental results of feature selection show that NQGA presents good search capability, rapid convergence, short computing time, and ability to avoid premature convergence effectively.