Pulse Repetition Interval Modulation Research Articles

Enhanced PRIM recognition using PRI sound and deep learning techniques.

Pulse repetition interval modulation (PRIM) is integral to radar identification in modern electronic support measure (ESM) and electronic intelligence (ELINT) systems. Various distortions, including missing pulses, spurious pulses, unintended jitters, and noise from radar antenna scans, often hinder the accurate recognition of PRIM. This research introduces a novel three-stage approach for PRIM recognition, emphasizing the innovative use of PRI sound. A transfer learning-aided deep convolutional neural network (DCNN) is initially used for feature extraction. This is followed by an extreme learning machine (ELM) for real-time PRIM classification. Finally, a gray wolf optimizer (GWO) refines the network's robustness. To evaluate the proposed method, we develop a real experimental dataset consisting of sound of six common PRI patterns. We utilized eight pre-trained DCNN architectures for evaluation, with VGG16 and ResNet50V2 notably achieving recognition accuracies of 97.53% and 96.92%. Integrating ELM and GWO further optimized the accuracy rates to 98.80% and 97.58. This research advances radar identification by offering an enhanced method for PRIM recognition, emphasizing the potential of PRI sound to address real-world distortions in ESM and ELINT systems.

Attention driven CWT-deep learning approach for discrimination of Radar PRI modulation

With the proliferation of radio frequency (RF) systems and radar applications, Electronic Warfare (EW) is receiving increasing importance. The analysis of the radar signals is a critical EW task that decides the nature of counter employments. In an Electronic Support (ES) system, the challenge is to detect hostile radiation sources efficiently and trigger a counter response. Detection of types of Pulse Repetition Interval (PRI) modulation of radar signal significantly facilitates the manifestation of RF emitters during recognition which is difficult in a dense EW environment. Recent developments in artificial intelligence (AI) methods suggest that this emerging technology can be effective for such purposes. In this direction, an automatic approach for recognizing several kinds of complex PRI modulation based on Continuous Wavelet Transform (CWT) and a combination of the vanilla Convolutional Neural Network (CNN), a multi-head self-attention (MHSA) mechanism and the popular Long Short-Term Memory (LSTM) is proposed. The CWT is used to decompose the PRI modulation sequence and obtain different time–frequency components. Further, aided by the proposed CNN-MHSA-LSTM combination, the features extracted from the CWT 2D-scalograms are used to execute PRI modulation discrimination. In this method, the vanilla CNN is employed for the extraction of deep features to figure out the class details while capturing the spatial attributes. Thereafter, to improve the discriminative power of the entire framework a MHSA mechanism is used. The temporal attributes are acquired by the LSTM which works in concert with the CNN for executing the detection of the PRI classes based on the extracted features. Also to assess the effectiveness of the proposed method, three models based on ResNet, popular CNN and SqueezeNet are implemented for benchmark comparison in terms of overall performance and complexity. The simulation results show that the proposed method enhances performance and achieves robustness in the noise-filled and imperfect channel knowledge environment. The best recognition accuracy is 98.3% with 50% spurious pulses in the environment which fluctuates with imperfect channel knowledge cases.

PRI modulation recognition and sequence search under small sample prerequisite

Pulse repetition interval (PRI) modulation recognition and pulse sequence search are significant for effective electronic support measures. In modern electromagnetic environments, different types of inter-pulse slide radars are highly confusing. There are few available training samples in practical situations, which leads to a low recognition accuracy and poor search effect of the pulse sequence. In this paper, an approach based on bi-directional long short-term memory (BiLSTM) networks and the temporal correlation algorithm for PRI modulation recognition and sequence search under the small sample prerequisite is proposed. The simulation results demonstrate that the proposed algorithm can recognize unilinear, bilinear, sawtooth, and sinusoidal PRI modulation types with 91.43% accuracy and complete the pulse sequence search with 30% missing pulses and 50% spurious pulses under the small sample prerequisite.

Pulse repetition interval modulation recognition using deep CNN evolved by extreme learning machines and IP-based BBO algorithm

Pulse repetition interval modulation (PRIM) recognition is a critical task in electronic intelligence (ELINT) and electronic support measure (ESM) systems for detecting radar threats accurately. However, PRI recognition is a complex issue due to missing and spurious pulses, resulting in noisy PRI pattern changes in real environments. To address this problem, this paper proposes a novel approach that recognizes the five common types of PRIM through a four-phase process. In the first phase, a deep convolutional neural network (DCNN) is used as a feature extractor. Then, extreme learning machines (ELMs) are used for real-time recognition of the PRIM patterns in the second phase. In the third phase, we employ the biogeography-based optimizer (BBO) to enhance the network’s robustness by optimizing the connection weights and biases. To address the increasing complexity of the model, we introduce an optimized variable-length internet protocol-based BBO (VBBO) in the fourth phase. In this approach (i.e., DCNN-VBBO-ELM), each layer of DCNN is encoded by an IP address into a habitat of VBBO in the same sequence as the DCNN layers. To evaluate the proposed method, we develop a real experimental dataset consisting of five common PRI patterns. Our approach achieves a final accuracy of 97.05%, which is better than other ELM-based benchmark models. Moreover, the proposed model requires only 27 s of training time to process 50,000 training images, confirming its real-time capabilities. In conclusion, our proposed approach improves PRI recognition by leveraging DCNN, ELM, and VBBO, resulting in a more accurate and robust real-time radar PRI classifier.

Radar pulse repetition interval modulation recognition with combined net and domain-adaptive few-shot learning

Automatically identifying pulse repetition interval (PRI) patterns is an important task for electronic support measure (ESM) systems in a complex electromagnetic environment. Existing learning-based approaches need handcrafted features to increase recognition accuracy and are unable to deal with the potential mismatch between the training set and testing samples. In this paper, we design a multiscale convolution block with a vectorized embedding and squeeze-and-excitation mechanism for feature extraction. Accordingly, we also consider the problem of domain shift, proposing a nonlinear, metric-based relation module to make the network quickly adapt to the new environment with an extremely low number of target training samples. Experiments on the PRI simulation datasets not only show the superiority of our network architecture but also prove the effectiveness of the relation module and our training method on domain generalization.

Parametric model‐based deinterleaving of radar signals with non‐ideal observations via maximum likelihood solution

In modern electromagnetic environments, as the number of radars continuously increasing, the intercepted interleaving signals become more complex and are easily affected by different non-ideal factors. However, the widely used deinterleaving methods based on the pulse repetition interval (PRI) are difficult to effectively deinterleave signals with complex PRI modulation and are very sensitive to the received signal quality. To solve the deinterleaving problem in these complex situations, this paper proposes a parametric model-based deinterleaving solution consisting of two cascaded stages. The proposed method does not require priors such as the number of sources and corresponding modulation types, which makes it more suitable for complex environments in non-cooperative scenarios. In addition, it can adapt to complex non-ideal situations, including parameter measurement noise, missing pulse and spurious pulse conditions. Simulation results show that the deinterleaving precision of the proposed method outperforms baseline methods in non-ideal conditions and achieves 99.33% accuracy.

Deep ToA Mask Based Recursive Radar Pulse Deinterleaving

In a complex electromagnetic environment, multiple radar signals of various modes are densely interleaved. In this environment, radar parameters overlap seriously and change continuously over time. Traditional radar pulse deinterleaving algorithms face severe challenges such as parameters missing, pulse jitter, and the increasing number of electronic countermeasure devices. In this paper, we propose a recursive deinterleaving algorithm based on blind signal separation and deep learning to cope with such a situation. The Recursive Deinterleaving Network (RDN) of Deep ToA Mask (DTM) maps the ToA train to a suitable feature space first. ToA coefficient masks of each radar emitter are estimated with the local and global context information of the radar pulse feature. Then the RDN sorts out several radar pulse trains recursively with the help of dual-path attention. It also predicts the number of emitters with nearly 100% accuracy and handles the unknown Pulse Repetition Interval (PRI) situation. More accurate pulse deinterleaving results can be obtained if the DTM utilizes more radar parameters through proper pre-processing fine-tuning and post-processing re-clustering. The processing steps of the DTM are introduced in detail. The simulation shows it can achieve 97% sorting accuracy for multi-pulse interleaved radar train with jitter PRI and pulse missing. The DTM algorithm can also deal with the interleaved radar signals of different PRI modulations by re-clustering with noisy PDW information. On the premise of knowing the modulation type or PRI information, the pulse train deinterleaving accuracy of multi-modulation emitters is higher.

Model-Based Time Series Clustering and Interpulse Modulation Parameter Estimation of Multifunction Radar Pulse Sequences

Multifunction radars (MFRs) are sophisticated sensors with significant intelligence, flexibility, and agility. It is important to analyze the system behavior and interpret the intentions of an MFR through the recognition of consecutive work modes in a pulse sequence. With the rapid development of MFRs, the agility capabilities in work mode modulations and the modulation parameters have increased, making the recognition task more challenging. Therefore, it is essential to develop new methods that are more adaptive and less depended on prior information. This article proposes a new method for the time series clustering of MFR work modes named as model-based radar time series clustering. The proposed method considers the variable modulation parameters of submode and employs three algorithms for different assumptions of progressive availability of the priors of a noncooperative MFR. The experiments with four typical pulse repetition interval modulations simulation samples validate the feasibility and the superior performance of the proposed method over the state-of-the-art pulse sequence clustering methods for recognition of MFR work modes.

Automatic recognition of pulse repetition interval modulation using temporal convolutional network

Pulse Repetition Interval (PRI) modulation recognition is a key issue in radar identification process in modern electronic intelligent (ELINT) and electronic support measure (ESM) systems. In this study, a novel approach based on the intrinsic property of the temporal convolutional network (TCN) is presented for PRI modulation type recognition. Since a causal TCN is used for this purpose, the method is suitable for online ESM and ELINT analysis. The simulation results show that the method accurately classifies seven types of PRI modulation including simple, dwell and switch, stagger, jitter, agile, sliding and periodic modulations in realistic scenarios. In order to investigate the performance of the method when the problem of missing or spurious pulses occurs, the impacts of these problems are examined, separately and together, on the recognition process. It is shown that the method works effectively even with a relatively high percentage of missing and/or spurious pulses (up to 30%). The method also works effectively in the presence of unintentional jitter and large outliers, which can be due to radar antenna scan. The experimental results on real data confirm that the proposed method performs accurately and effectively in real world scenarios.

A Unified Method for Deinterleaving and PRI Modulation Recognition of Radar Pulses Based on Deep Neural Networks

In the modern electronic warfare signal environment, multiple radar signals of high density are mixed and received, and separating them into signals for each emitter is an essential step for emitter identification. Each radar has its own pulse repetition interval (PRI), which is a key parameter for deinterleaving pulse trains. The PRI is modulated in various forms depending on the purpose of the radar operation, and analyzing the mean PRI and the modulation type of PRI is the core of electronic warfare signal processing. Many existing papers have tried separate independent approaches for deinterleaving and for PRI modulation recognition. However, many distortions are unintentionally generated in the process of extracting the pulse train using the PRI estimated through deinterleaving for the PRI modulation recognition. This degrades the modulation recognition performance. In this paper, we propose a unified method for the deinterleaving and PRI modulation recognition of radar pulses using deep learning-based multitasking learning. The simulation results demonstrate the good performance of the proposed method for deinterleaving and modulation recognition, compared to the conventional method, and prove that the proposed method is robust in noisy radar signal environments.

ACSE Networks and Autocorrelation Features for PRI Modulation Recognition

Pulse repetition interval (PRI) modulation recognition plays an important role in electronic warfare. Conventional recognition methods based on handcrafted features and elaborate threshold values suffer from the accuracy for multiple PRI modulations at low signal-to-noise ratio (SNR) with high percentages of missing pulses. In this letter, a method based on Asymmetric Convolution Squeeze-and-Excitation (ACSE) networks and features in autocorrelation domain is proposed to recognize six PRI modulation modes automatically. First, features in the time domain, frequency domain and autocorrelation domain are converted to images. Then the images are input into ACSE networks which can extract and learn deep features without complex data pre-processing. Finally, a linear layer will output modulation modes directly. Via simulations, robustness of autocorrelation features is proved. The simulation results also demonstrate that the proposed recognition method can achieve higher than 91% accuracy at -12 dB under normal conditions for six modulations and higher than 95% at -4 dB under extreme conditions. Compared with the conventional SVM and three CNN methods, ACSE networks outperform at low SNRs under extreme conditions.

Deinterleaving of Pulse Streams With Denoising Autoencoders

Analyzing radar signals is an important task in operating electronic support measure systems. The received signals in the real electromagnetic environment often originate from multiple emitters and must be separated for further processing. Pulses from important target emitters with known parameters should be picked out first. To solve the problem, time-of-arrival (TOA) deinterleaving may be performed to extract signals from a certain emitter by learning the pulse repetition interval (PRI) modulation that makes up the signal. However, conventional deinterleaving methods only work with simple PRI modulations; their performance degrades in noisy environments. A novel approach based on denoising autoencoders for TOA deinterleaving was developed in this article. The inner patterns of pulse-of-interest sequences were learned by the proposed denoising autoencoders to generate output sequences from well-trained autoencoders. Simulation results show that the proposed method outperforms conventional methods, especially in environments with high lost and spurious pulse ratios.

PRI Modulation Recognition Based on Squeeze-and-Excitation Networks

Pulse repetition interval (PRI) modulation recognition is a significant means to analyze radar working statuses and missions in Electronic Support system. Traditional methods may be insufficient to accurately recognize complex PRI at low SNR with high percentages of missing pulses. In this letter, an approach based on Squeeze-and-Excitation networks (SE-Net) and the autocorrelation functions for PRI modulation recognition automatically is proposed. Firstly, the features of six PRI modulation types in the autocorrelation domain are converted into images by calculating instantaneous autocorrelation functions. Then, the images will be the input of SE-Net which will automatically learn about deep features of different PRI modulation modes. Finally, SE-Net will output PRI modulation modes directly. Simulation results show that SE-Net is robust to the noise and missing pulses. The accuracies for all PRI modulation modes are more than 95% at −10dB with 30% missing pulses and more than 96% at −2dB with 50% missing pulses. Compared with traditional method and other networks, SE-Net can achieve better recognition performance at low SNR and high percentages of missing pulses.

Attention-Based Radar PRI Modulation Recognition With Recurrent Neural Networks

Analyzing radar signals is a critical task in modern Electronic Warfare (EW) environments. However, the pulse streams emitted by radars have flexible features and complex patterns which are difficult to be identified from a statistical perspective. To solve this problem, pulse repetition interval (PRI) is used as a distinguishing parameter of emitters to be identified. However, traditional PRI modulation recognition methods can only deal with simple PRI modulations and their performance will further degrade with the increasing number of emitters or noisy environments. In this paper, we introduce an attention-based recognition framework based on recurrent neural network (RNN) to categorize pulse streams with complex PRI modulations and in environments with high ratios of missing and spurious pulses. Simulation results show that our model is robust to noisy environments and has a better performance than conventional methods.

A wavelet-based feature set for recognizing pulse repetition interval modulation patterns

This paper presents a new feature set for the problem of recognizing pulse repetition interval (PRI) modulation patterns. The recognition is based upon the features extracted from the multiresolution decomposition of different types of PRI modulated sequences. Special emphasis is placed on the recognition of jittered and stagger type PRI sequences due to the fact that these types of PRI sequences appear predominantly in modern electronic warfare environments for some specic mission requirements and recognition of them is heavily based on histogram features. We test our method with a broad range of PRI modulation parameters. Simulation results show that the proposed feature set is highly robust and separates jittered, stagger, and other modulation patterns very well. Especially for the stagger type of PRI sequences, wavelet-based features outperform conventional histogram-based features. Advantages of the proposed feature set along with its robustness criteria are analyzed in detail.

A Simple Method for Pulse Repetition Interval Estimation and Tracking Radar Pulse Trains with Complex Pulse Repetition Interval Modulations

A Simple Method for Pulse Repetition Interval Estimation and Tracking Radar Pulse Trains with Complex Pulse Repetition Interval Modulations

A Robust Method for Recognition of Complicated Pulse Repetition Interval Modulations

A Robust Method for Recognition of Complicated Pulse Repetition Interval Modulations

Hierarchical classification of dynamically varying radar pulse repetition interval modulation patterns

The central purpose of passive signal intercept receivers is to perform automatic categorization of unknown radar signals. Currently, there is an urgent need to develop intelligent classification algorithms for these devices due to emerging complexity of radar waveforms. Especially multifunction radars (MFRs) capable of performing several simultaneous tasks by utilizing complex, dynamically varying scheduled waveforms are a major challenge for automatic pattern classification systems. To assist recognition of complex radar emissions in modern intercept receivers, we have developed a novel method to recognize dynamically varying pulse repetition interval (PRI) modulation patterns emitted by MFRs. We use robust feature extraction and classifier design techniques to assist recognition in unpredictable real-world signal environments. We classify received pulse trains hierarchically which allows unambiguous detection of the subpatterns using a sliding window. Accuracy, robustness and reliability of the technique are demonstrated with extensive simulations using both static and dynamically varying PRI modulation patterns.

Recognition of PRI Modulation Types of Radar Signals Using the Autocorrelation

In electronic warfare support systems, the analysis of PRI (Pulse Repetition Interval) modulation characteristics for a radar pulse signal has attracted much interest because of the problem of the identification ambiguity in dense electronic warfare signal environments. A new method of recognizing the PRI modulation type of a radar pulse signal is proposed for electronic warfare support. The proposed method recognizes the PRI modulation types using classifiers based on the property of the autocorrelation of the PRI sequences for each PRI modulation type. In addition, the proposed method estimates the PRI modulation period for the PRI modulation type with the periodicity. Simulation results are presented to show the performance of the proposed method.

Analysis of the digital MTI filter with random PRI

Modulation of the radar pulse repetition interval (PRI) is a conventional technique to improve the target velocity coverage in a moving target indicator (MTI) filter. Typically, a given pattern of interpulse intervals is periodically repeated (staggered PRI). Additionally, the staggered PRI gives us some protection against PRI analysis in an electronic intelligence environment. Nevertheless, this protection is limited by the repetitive character of the PRI modulation pattern. Much greater protection can be achieved by means of a random selection of the PRI between fixed bounds.This paper presents an analysis of the digital MTI for two types of PRI random generation. In the first type, small deviations with respect to a mean PRI are randomly generated (random deviation method, RDM). In the second type, the intervals themselves are randomly selected random interval method, RIM). We derive for both cases the relationship between the velocitydependent gain factor and the probability density function (PDF) of the corresponding random variables. This allows us to predict the influence of the PDF in the expected performance. Specific design strategies that take into account practical constraints, like the maximum unambiguous range and the dwell time, are also proposed for each method.