Intra-pulse Modulation Recognition Research Articles

Bayesian Prototype Learning for Few-Shot Radar Signal Intra-Pulse Modulation Recognition

Bayesian Prototype Learning for Few-Shot Radar Signal Intra-Pulse Modulation Recognition

Intra-Pulse Modulation Recognition of Radar Signals Based on Efficient Cross-Scale Aware Network.

Radar signal intra-pulse modulation recognition can be addressed with convolutional neural networks (CNNs) and time-frequency images (TFIs). However, current CNNs have high computational complexity and do not perform well in low-signal-to-noise ratio (SNR) scenarios. In this paper, we propose a lightweight CNN known as the cross-scale aware network (CSANet) to recognize intra-pulse modulation based on three types of TFIs. The cross-scale aware (CSA) module, designed as a residual and parallel architecture, comprises a depthwise dilated convolution group (DDConv Group), a cross-channel interaction (CCI) mechanism, and spatial information focus (SIF). DDConv Group produces multiple-scale features with a dynamic receptive field, CCI fuses the features and mitigates noise in multiple channels, and SIF is aware of the cross-scale details of TFI structures. Furthermore, we develop a novel time-frequency fusion (TFF) feature based on three types of TFIs by employing image preprocessing techniques, i.e., adaptive binarization, morphological processing, and feature fusion. Experiments demonstrate that CSANet achieves higher accuracy with our TFF compared to other TFIs. Meanwhile, CSANet outperforms cutting-edge networks across twelve radar signal datasets, providing an efficient solution for high-precision recognition in low-SNR scenarios.

Radar Emitter Signal Intra-Pulse Modulation Open Set Recognition Based on Deep Neural Network

Radar emitter signal intra-pulse modulation recognition is important for modern electronic reconnaissance systems to analyze target radar systems. In the actual environment, the intra-pulse modulations of the sampled radar emitter signals contain not only the known types in the library but also the unknown types. Therefore, the existing recognition methods, which are based on a closed set, cannot recognize the unknown samples. In order to solve this problem, in this paper, we proposed a method for radar emitter signal intra-pulse modulation open set recognition. The proposed method could classify the known modulations and identify the unknown modulation by using an original deep neural network-based recognition model trained on a closed set, estimating the signal-to-noise ratio, and calculating the reconstruction loss by an encoder–decoder model. For a given sample, the original deep neural network-based recognition model will label it as a certain known class temporarily. By estimating the SNR of the sample and calculating the reconstruction loss by inputting the sample to the corresponding encoder–decoder model related to the temporary predicted known class, whether the sample belongs to the predicted temporary known class or the unknown class will be confirmed. Experiments were conducted with five different openness conditions. The experimental results indicate that the proposed method has good performance on radar emitter signal intra-pulse modulation open set recognition.

An Intrapulse Modulation Recognition Method for Low Probability of Interception Signals Based on the Coprime DTFrFT Spectral Receiver

An Intrapulse Modulation Recognition Method for Low Probability of Interception Signals Based on the Coprime DTFrFT Spectral Receiver

An Application of Analytic Wavelet Transform and Convolutional Neural Network for Radar Intrapulse Modulation Recognition.

This article analyses the possibility of using the Analytic Wavelet Transform (AWT) and the Convolutional Neural Network (CNN) for the purpose of recognizing the intrapulse modulation of radar signals. Firstly, the possibilities of using AWT by the algorithms of automatic signal recognition are discussed. Then, the research focuses on the influence of the parameters of the generalized Morse wavelet on the classification accuracy. The paper's novelty is also related to the use of the generalized Morse wavelet (GMW) as a superfamily of analytical wavelets with a Convolutional Neural Network (CNN) as classifier applied for intrapulse recognition purposes. GWT is used to obtain time-frequency images (TFI), and SqueezeNet was chosen as the CNN classifier. The article takes into account selected types of intrapulse modulation, namely linear frequency modulation (LFM) and the following types of phase-coded waveform (PCW): Frank, Barker, P1, P2, and Px. The authors also consider the possibility of using other time-frequency transformations such as Short-Time Fourier Transform(STFT) or Wigner-Ville Distribution (WVD). Finally, authors present the results of the simulation tests carried out in the Matlab environment, taking into account the signal-to-noise ratio (SNR) in the range from -6 to 0 dB.

Intra-pulse modulation recognition of radar signals based on multi-feature random matching fusion network

Intra-pulse modulation recognition of radar signals based on multi-feature random matching fusion network

Radar Signals Intrapulse Modulation Recognition Using Phase-Based STFT and BiLSTM

The goal of Radar Emitter Recognition (RER) is to extract the features of a received emitter signal. It has become a critical issue as new radar types are emerging and the electromagnetic environment is becoming more dense and complex. Deep Neural Networks (DNNs) have recently proven effective for emitter identification, however, recognition of phase-coded waveforms at a low Signal to Noise Ratio (SNR) remains a challenge. In this paper, a novel phase-based RER approach using Short Time Fourier Transform (STFT) and Bidirectional Long Short Term Memory (BiLSTM) is proposed while enhancing ability of learning features from noisy signals. The phase spectrum of phase-coded signals is analyzed in contrast to the amplitude spectrum used in the state-of-the-art approaches in the literature. The derived phase-based features are directly provided as an input to the proposed BiLSTM architecture. A fully connected layer follows the BiLSTM layer. Finally, a softmax classifier is employed to accomplish the recognition task. Six distinct types of phase-coded waveforms degraded by Additive White Gaussian Noise (AWGN) with SNRs ranging from -8dB to 8dB are simulated. The suggested method in this research involves simple pre-processing and exhibits overall recognition accuracy of more than 90% at SNR of -2dB.

Intra-Pulse Modulation Recognition of Dual-Component Radar Signals Based on Deep Convolutional Neural Network

In an ever-increasingly complicated electromagnetic environment with explosive radar signals density, accurate and fast recognition of dual-component radar signals has become an urgent problem in the current radar reconnaissance system. This letter proposes a novel multi-class learning framework based on deep convolutional neural network (DCNN) for recognizing eight types of randomly overlapping dual-component radar signals. The framework mainly includes dual-component radar signals preprocessing, DCNN model that aimed to extract more effective dual-component radar signals features, and multi-class classification. The results shown that the average classification accuracy of dual-component radar signals can be up to 96.17% when the signal-to-noise ratio (SNR) is −8 dB, which demonstrates the superior performance over others. The proposed model possesses the larger improvement for 4FSK, BPSK, EQFM, and FRANK, especially at the lower SNR. This work provides a sound guidance for further improving multi-component radar signals recognition.

Deep residual learning in modulation recognition of radar signals using higher-order spectral distribution

Automatically recognizing intra-pulse modulation of radar signals is a significant survival technique in electronic intelligence systems. To avoid the dependence on feature selection and realize the intelligent intra-pulse modulation recognition of various radar signals under low signal-to-noise ratios (SNRs), this paper develops a novel intra-pulse modulation recognition method based on the high-order spectrums of radar signals. Automatic soft thresholding is implemented in the deep residual network to adaptively eliminate redundant information in the process of feature learning and improve the learning effect of valuable features in distribution images of corresponding third-order spectrums. The extensive simulations compared with the other four methods further reveal the excellent classification performance of the proposed method. The proposed approach still achieves an overall probability of successful recognition of 93.5% for eight kinds of modulation signals, even when the SNR is just −8 dB. Outstanding performance proves the superiority and robustness of the proposed method.

GCPS: A CNN Performance Evaluation Criterion for Radar Signal Intrapulse Modulation Recognition

In radar intrapulse modulation recognition through convolutional neural networks (CNN), the recognition rate vis-a-vis varying signal-to-noise ratios (SNRs) is utilized to analyze the performance of networks. However, three shortcomings exist in this realm, (1) Proportional increase in recognition rate up to 100%, due to SNR increase, leading to saturation. (2) The dependency of recognition rate on the test set. (3) Recognition rate failing to substantiate the increase in SNR and its manifestation in CNN performance enhancement. Foregone in view, this letter proposes an evaluation criterion to evaluate CNN performance to address the shortcomings above. We propose Grad-CAM position score-Internal Benchmark (GCPS-IB) to address (1), (2), and Grad-CAM position score-External Benchmark (GCPS-EB) to address (3). Finally, we utilize GCPS to evaluate and compare the performance of existing GoogLeNet and ResNet-18 to verify the efficacy. The proposed criterion establishes its efficacy by addressing the shortcomings mentioned above, hence augmenting the recognition rate.

Radar Signal Intra-Pulse Modulation Recognition Based on Convolutional Neural Network and Deep Q-Learning Network

Intra-pulse modulation recognition of radar signals is an important part of modern electronic intelligence reconnaissance and electronic support systems. With the increasing density of radar signals, the analysis and processing of multi-component radar signals have become an urgent problem in the current radar reconnaissance system. In this paper, an intra-pulse modulation recognition approach for single-component and dual-component radar signals is proposed. First, in order to adapt to the time-frequency energy distribution characteristics of various radar signals, we propose to extract the time-frequency images (TFIs) of received signals by Cohen class time-frequency distribution (CTFD) with multiple kernel functions. Besides, the image processing methods are used to suppress noise and adjust the size and amplitude of the TFIs. Second, we design and pre-train a TFI feature extraction network for radar signals based on a convolutional neural network (CNN). Finally, to improve the probability of successful recognition (PSR) of the recognition system in the pulse overlapping environment, a multi-label classification network based on a deep Q-learning network (DQN) is explored. Besides, two sub-networks take TFIs based on special kernel functions as input and re-judge the recognition results of some specific signals to further enhance the recognition effect of the recognition system. The proposed approach can identify 8 kinds of random overlapping radar signals. The simulation results show that the overall PSR of dual-component radar signals and single-component radar signals can reach 94.83% and 94.43%, respectively, when the signal-to-noise ratio (SNR) is −6 dB.

Intra-Pulse Modulation Recognition for Fractional Bandlimited Signals Based on a Modified MWC-Based Digital Receiver

In this paper, we present a modified modulated wideband converter (MWC)-based digital receiver for fractional bandlimited signals, and further propose an intra-pulse modulation recognition method in discrete time fractional Fourier domain (DTFrFD) for the intercepted signals. The proposed digital receiver can move the cross-channel signal to the baseband and since the nonzero part of its available spectrum is narrower in DTFrFD than in discrete time Fourier domain (DTFD), so a better separation can be achieved by the proposed digital receiver than by original MWC- based receiver. Then, with the data acquired from the digital receiver, we propose an intra-pulse modulation recognition method based on the optimal transformation order and the spectral kurtosis (SK) in DTFrFD for the six types of fractional bandlimited signals. In this algorithm, the optimal transformation order is tested to distinguish the encoded signals from the non-encoded signals, and then the SK is tested to determine the intra-pulse modulation type specially. The computational complexity of the proposed method is much lower than search-based methods. Meanwhile, since the SK of Gaussian signals is zero in DTFrFD, the proposed method shows better robustness than the original MWC discrete compressive sampling structure against SNR variation. Simulation results confirm the obtained analytical results.

Radar Signal Intra-Pulse Modulation Recognition Based on Convolutional Denoising Autoencoder and Deep Convolutional Neural Network

Radar signal intra-pulse modulation recognition is an important technology in electronic warfare. A radar signal intra-pulse modulation recognition method based on convolutional denoising autoencoder (CDAE) and deep convolutional neural network (DCNN) is proposed in this paper. First, we use Cohen’s time-frequency distribution to convert radar signals into time-frequency images (TFIs). Then image preprocessing is applied to TFIs, including bilinear interpolation and amplitude normalization. Next, we design a CDAE to denoise and repair TFIs. Finally, we design a deep convolutional neural network based on Inception architecture to identify the processed TFIs. Simulation results demonstrate that CDAE effectively reduces the interference of noise on TFIs classification, and improves the classification performance at a low signal-to-noise ratio (SNR). The DCNN architecture we designed makes good use of computing resources and has a good classification effect. The approach has good noise immunity and generalization. It can classify twelve kinds of modulation signals and an overall probability of successful recognition is more than 95% when the SNR is −9 dB.

Radar Signal Intra-Pulse Modulation Recognition Based on Convolutional Neural Network

In this paper, to solve the problem of the low recognition rate of the existing approaches at low signal-to-noise ratio (SNR), an intra-pulse modulation recognition approach for radar signal is proposed. The approach identifies the modulation of radar signals using the techniques of time-frequency analysis, image processing, and convolutional neural network (CNN). Through Cohen class time-frequency distribution (CTFD), the time-frequency images (TFIs) of received signals are extracted. In order to obtain the high-quality TFIs of received signals, we introduce a new kernel function for the CTFD, which has stronger anti-noise ability than Choi–Williams time-frequency distribution. A series of image processing techniques, including 2-D Wiener filtering, bilinear interpolation, and Otsu method, are applied to remove the background noise of the TFI and obtain a fixed-size binary image that contains only morphological features of the TFI. We design a CNN classifier to identify the processed TFIs. The proposed approach can identify up to 12 kinds of modulation signals, including frequency modulation, phase modulation, and composite modulation. Simulation results show that, for 12 kinds of modulation signals, the proposed approach achieves an overall probability of successful recognition of 96.1% when SNR is −6 dB.

LPI Radar Waveform Recognition Based on Multi-Branch MWC Compressed Sampling Receiver

In this paper, a new wideband digital receiver based on the modulated wideband converter compressed sampling (CS) system is proposed to replace a conventional wideband digital receiver, solve the cross-channel signal problem, and achieve intra-pulse modulation recognition for the low probability of intercept (LPI) radar signals. The proposed receiver uses cyclic-shifted pseudo-random sequences to mix the received signals to baseband. The mixed signals are low-pass filtered and down-sampled to obtain compressed sampling data containing the full information of the received signals. Since the phases of the multi-branch CS data are designed to change regularly, we propose a phase correction factor to correct the phases of the multi-branch CS data, which can be superposed to increase the output signal-to-noise ratio (SNR). Then, a recognition method based on the short-time Fourier transform (STFT) and spectrum energy focusing rate tests are proposed. First, the spectrum modulation bandwidth based on the STFT spectrum of the superposed CS data is tested to distinguish phase modulation signals and frequency modulation signals approximately. Then, the spectrum energy focusing rate of the superposed CS data is tested to determine the intra-pulse modulation type specifically. Finally, the superiority of the proposed recognition system for LPI radar signals and cross-channel signals is demonstrated by simulations and analysis. Simulation results show that the overall ratio of the successful recognition (RSR) is 100% when the SNR is greater than 0 dB.

Intra-pulse modulation recognition using short-time ramanujan Fourier transform spectrogram

Intra-pulse modulation recognition under negative signal-to-noise ratio (SNR) environment is a research challenge. This article presents a robust algorithm for the recognition of 5 types of radar signals with large variation range in the signal parameters in low SNR using the combination of the Short-time Ramanujan Fourier transform (ST-RFT) and pseudo-Zernike moments invariant features. The ST-RFT provides the time-frequency distribution features for 5 modulations. The pseudo-Zernike moments provide invariance properties that are able to recognize different modulation schemes on different parameter variation conditions from the ST-RFT spectrograms. Simulation results demonstrate that the proposed algorithm achieves the probability of successful recognition (PSR) of over 90% when SNR is above -5 dB with large variation range in the signal parameters: carrier frequency (CF) for all considered signals, hop size (HS) for frequency shift keying (FSK) signals, and the time-bandwidth product for Linear Frequency Modulation (LFM) signals.

An Approach to Intra-Pulse Modulation Recognition Based on the Ambiguity Function

Based on the ambiguity function, an approach to intra-pulse modulation recognition is presented. This paper defines the frequency energy accumulation function (FEAF) and time-domain extremum function (TEF) to describe the Doppler frequency shift and time delay properties of signals, respectively. This approach can recognize six types of signals by four characteristic parameters. Simulation results show that the probability of successful recognition (PSR) can reach 90% when the signal to noise ratio (SNR) is above −1 dB.