Electronic Intelligence Systems Research Articles

Investigating the effects of destructive factors on pulse repetition interval modulation type recognition using deep convolutional neural networks based on transfer learning

AbstractAutomation and self‐sufficiency in the complex environment of modern electronic warfare (EW) are critical and necessary issues in electronic intelligence and support systems to detect real‐time and accurate threat radars. The task of these systems is to search, discover, analyse, and identify the parameters of radar signals. However, recognition pulse repetition interval (PRI) modulation is challenging in natural environments due to destructive factors, including missing pulses (MP), spurious pulses (SP), and large outliers (LO) (caused by antenna scanning), which lead to noisy sequences of PRI variation patterns. The current article examines the effects of destructive factors on recognising PRI modulation in radar signals using deep convolutional neural networks (DCNNs). The article uses simulations based on the actual environment to generate data and consider destructive factors with different percentages. The number of images obtained by applying the sum of destructive factors for each range of destructive factors (with different percentages) considered is 30,000. It is common for six types of modulation. Then, the DCNN models, including VGG16, ResNet50V2, InceptionV3, Xception, and MobileNetV2, are trained using the transfer learning method. The simulation results show that the accuracy of training and testing the models decreases significantly with the increase in the percentage of destructive factors. Also, the effects of the model type on the performance of the models have been investigated, and the results have shown that some models are more resistant to destruction and retain more accuracy. Finally, this analysis shows that to improve the performance of deep neural network (DNN) techniques in the face of changes caused by destructive factors, it is necessary to pay attention to these factors and apply appropriate strategies.

On a Closer Look of a Doppler Tolerant Noise Radar Waveform in Surveillance Applications.

The prevalence of Low Probability of Interception (LPI) and Low Probability of Exploitation (LPE) radars in contemporary Electronic Warfare (EW) presents an ongoing challenge to defense mechanisms, compelling constant advances in protective strategies. Noise radars are examples of LPI and LPE systems that gained substantial prominence in the past decade despite exhibiting a common drawback of limited Doppler tolerance. The Advanced Pulse Compression Noise (APCN) waveform is a stochastic radar signal proposed to amalgamate the LPI and LPE attributes of a random waveform with the Doppler tolerance feature inherent to a linear frequency modulation. In the present work, we derive closed-form expressions describing the APCN signal's ambiguity function and spectral containment that allow for a proper analysis of its detection performance and ability to remove range ambiguities as a function of its stochastic parameters. This paper also presents a more detailed address of the LPI/LPE characteristic of APCN signals claimed in previous works. We show that sophisticated Electronic Intelligence (ELINT) systems that employ Time Frequency Analysis (TFA) and image processing methods may intercept APCN and estimate important parameters of APCN waveforms, such as bandwidth, operating frequency, time duration, and pulse repetition interval. We also present a method designed to intercept and exploit the unique characteristics of the APCN waveform. Its performance is evaluated based on the probability of such an ELINT system detecting an APCN radar signal as a function of the Signal-to-Noise Ratio (SNR) in the ELINT system. We evaluated the accuracy and precision of the random variables characterizing the proposed estimators as a function of the SNR. Results indicate a probability of detection close to 1 and show good performance, even for scenarios with a SNR slightly less than -10 dB. The contributions in this work offer enhancements to noise radar capabilities while facilitating improvements in ESM systems.

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.

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The purpose of the article is to assess the capabilities of mobile electronic intelligence means of the tactical link of the sector of security and defence forces of Ukraine, to develop recommendations on the prospects for their development, to analyse the use of electronic intelligence systems and means of the russian federation army during the so-called "Special Military Operation" against Ukraine. The capabilities of means of radio-electronic intelligence in service with the Armed Forces of Ukraine and the National Guard of Ukraine are presented. A comparative analysis of the tactical and technical characteristics of Ukrainian and foreign-made equipment is carried out, as well as a brief analysis of the radio-electronic warfare equipment of the aggressor country and the results of their use. On the basis of the analysis, recommendations are made on the prospects for the use and development of electronic intelligence means of the tactical link of the forces of the security and defence sector of Ukraine.

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.

LPI Waveform Recognition Using Adaptive Feature Construction and Convolutional Neural Networks

Low probability of intercept (LPI) radar waveform recognition is one of the crucial functions in electronic intelligence systems. Advances in artificial intelligence promote the performance of the LPI waveform recognition with various signal features defined with analytical expressions. However, noisy LPI waveform recognition is still a challenge for traditional approaches even with noise elimination techniques, especially for the heavily contaminated noisy LPI signals. Recently, the adaptive analysis techniques, such as variational mode decomposition (VMD) and empirical mode decomposition (EMD) provide potential methods that explore the inherent features of signals and formulate adaptive features for signal recognition. In this article, we propose an adaptive feature construction framework that utilizes both the adaptive features (via VMD and EMD) and predefined analytical features (via Wigner–Ville distribution, Choi–William distribution, and wavelet analysis) to construct the fusion feature, which is further applied on the convolutional neural networks-based LPI waveform recognition system. The experimental results show that the proposed feature adaptive LPI network-based exploitation (FALPINE) approach achieves higher probability of correct classification than the state-of-the-art works, which demonstrates the superior performance of the proposed approach.

Image Segmentation for Radar Signal Deinterleaving Using Deep Learning

Passive systems, such as electronic intelligence and electronic support measures systems, aim to extract necessary information from the received radar signals for situational awareness. To achieve this, the system must first deinterleave the radar signals simultaneously coming from different emitters, so that the pulse repetition interval (PRI) patterns will be revealed for further analysis and identification purposes. PRI transform is a well-known deinterleaving method that utilizes the complex autocorrelation function. There are two main versions of the method. The initial version detects only constant PRI schemes, while the second modified version is capable of detecting varying PRI schemes as well. Miss detection of varying PRI patterns is the drawback for the first version, while producing harmonics, especially at high PRI levels, is the disadvantage of the second one. To alleviate these problems, we propose an image segmentation method based on deep learning. The developed preprocessing step uses both versions of the PRI transform outputs to generate 2-D time–PRI images of the collected radar emissions, so that constant and varying PRI patterns are revealed. The images are concatenated and fed to the proposed network, which uses a practicable U-Net structure. The output of the network directly estimates the PRI levels of the existing radars and the time duration of the transmission jointly. In addition to qualitative and quantitative experiments on the synthetic datasets, qualitative experiments are conducted on real measurements, in which we demonstrate that the proposed method effectively utilizes PRI transform in the preprocessing step and outperforms both versions of the PRI transform in terms of accuracy, Jaccard index, structural similarity, and PRI estimation error metrics.

Open-Metric for Unknown Signal Inference

The closed-set assumption that only known classes can be classified during testing is usually adopted in traditional signal recognition missions. It has been thoroughly explored and made huge progress in the past decade. However, it cannot cope with the unknown categories coming from the changing environment. As waveform diversity technology evolves by leaps and bounds, the existence of novel unknown classes is inevitable in the open-world environment, imposing a huge challenge to the non-cooperative electronic support system. To further satisfy the demands of modern electronic intelligence systems and sense new threats, it is urgent to find an approach that can tackle unknown signals. An Open-Metric strategy is proposed in this article to deduce the unknown classes from the intercepted signals. By virtue of metric learning methods, a plain backbone can be trained to offer more discriminative representations and a consistent proxy for each class. According to extreme value analysis, information about known classes can be converted into an estimated score of unknown classes, creating an open decision layer. The investigated Open-Metric approach demonstrates its superiority in unknown signal inference tasks, facilitating improvements in the intelligence and smartness of the electronic reconnaissance agents to cope with future cognitive electronic warfare.

An End-to-End Deep Learning Approach for State Recognition of Multifunction Radars.

With the widespread use of multifunction radars (MFRs), it is hard for the traditional radar signal recognition technology to meet the needs of current electronic intelligence systems. For signal recognition of an MFR, it is necessary to identify not only the type or individual of the emitter but also its current state. Existing methods identify MFR states through hierarchical modeling, but most of them rely heavily on prior information. In the paper, we focus on the MFR state recognition with actual intercepted MFR signals and develop it by introducing recurrent neural networks (RNNs) of deep learning into the modeling of MFR signals. According to the layered MFR signal architecture, we propose a novel end-to-end state recognition approach with two RNNs’ connections. This approach makes full use of RNNs’ ability to directly tackle corrupted data and automatically learn the features from input data. So, it is practical and less dependent on prior information. In addition, the hierarchical modeling method applied to the end-to-end network effectively restricts the scale of the end-to-end model so that the model can be trained with a small amount of data. Simulation results on a real MFR show the excellent recognition performance of our end-to-end approach with little prior information.

Instantaneous Frequency Estimation of Nonlinear FM Radar Signal Based on Multi-Scale Chirplet Path

Instantaneous frequency is an important parameter to non-linear frequency modulated (NLFM) signal in low probability intercept (LPI) radar. For electronic intelligence, it is very important to accurately estimate instantaneous frequency of NLFM signal. A multi-scale chirplet path pursuit (MCPP) method and its improved method are proposed for electronic intelligence systems to estimate instantaneous frequency of NLFM radar signal in this paper. Firstly, signal duration is divided into a set of dynamic time interval, multi-scale chirplet basis function is established on each time interval simultaneously. And then, projection coefficient in each dynamic interval is calculated basing on chirplet basis functions. And then, chirplet basis functions which have the largest projection coefficient with the analysis signal in each time interval are connected by path pursuit algorithm. Rough estimation of instantaneous frequency will be achieved by connecting the linear frequency of those chirplet basis functions. At last, to solve the problem that instantaneous frequency curve is not smooth for the impact of noise and chirplet errors, least square fitting method is used to further improve estimation accuracy. Experimental results show that, proposed improved MCPP algorithm is suitable for the instantaneous frequency of the NLFM radar signal at low SNR. Compared with time-frequency analysis method, it has higher estimation accuracy. Proposed method can also be applied to the instantaneous frequencies estimation of other NLFM signal without prior knowledge, such as seismic signals and fault diagnosis signals.

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.

Estimating the Instantaneous Frequency of Linear and Nonlinear Frequency Modulated Radar Signals-A Comparative Study.

Automatic modulation recognition plays a vital role in electronic warfare. Modern electronic intelligence and electronic support measures systems are able to automatically distinguish the modulation type of an intercepted radar signal by means of real-time intra-pulse analysis. This extra information can facilitate deinterleaving process as well as be utilized in early warning systems or give better insight into the performance of hostile radars. Existing modulation recognition algorithms usually extract signal features from one of the rudimentary waveform characteristics, namely instantaneous frequency (IF). Currently, there are a small number of studies concerning IF estimation methods, specifically for radar signals, whereas estimator accuracy may adversely affect the performance of the whole classification process. In this paper, five popular methods of evaluating the IF–law of frequency modulated radar signals are compared. The considered algorithms incorporate the two most prevalent estimation techniques, i.e., phase finite differences and time-frequency representations. The novel approach based on the generalized quasi-maximum likelihood (QML) method is also proposed. The results of simulation experiments show that the proposed QML estimator is significantly more accurate than the other considered techniques. Furthermore, for the first time in the publicly available literature, multipath influence on IF estimates has been investigated.

FPGA based Identification of Frequency and Phase Modulated Signals by Time Domain Digital Techniques for ELINT Systems

In this paper, a decision tree algorithm based on time-domain digital technique is developed for the identification and classification of diverse radar intra-pulse modulated signals for the electronic intelligence system in real-time. This includes linear frequency modulation, non-linear frequency modulation, stepped frequency modulation and bi-phase modulation. The received signal is digitised and the instantaneous phase and high accuracy instantaneous frequency are estimated. The instantaneous amplitude is also estimated to get the start and stop of the pulse. Instantaneous parameters are estimated using a moving autocorrelation technique. The proposed algorithm is employed on the instantaneous frequency and the modulation is identified. The modulation type and modulation parameter are important for unique radar identification when similar radars are operating in a dense environment. Simulations are carried out at various SNR conditions and results are presented. The model for algorithm is developed using a system generator and implemented in FPGA. These results are compared when the proposed algorithm is used with the existing digital in-phase and quadrature-phase (DIQ) technique of instantaneous frequency and amplitude estimation.

Modulation Recognition of Radar Signals Based on Adaptive Singular Value Reconstruction and Deep Residual Learning.

Automatically recognizing the modulation of radar signals is a necessary survival technique in electronic intelligence systems. In order to avoid the complex process of the feature extracting and realize the intelligent modulation recognition of various radar signals under low signal-to-noise ratios (SNRs), this paper proposes a method based on intrapulse signatures of radar signals using adaptive singular value reconstruction (ASVR) and deep residual learning. Firstly, the time-frequency spectrums of radar signals under low SNRs are improved after ASVR denoising processing. Secondly, a series of image processing techniques, including binarizing and morphologic filtering, are applied to suppress the background noise in the time-frequency distribution images (TFDIs). Thirdly, the training process of the residual network is achieved using TFDIs, and classification under various conditions is realized using the new-trained network. Simulation results show that, for eight kinds of modulation signals, the proposed approach still achieves an overall probability of successful recognition of 94.1% when the SNR is only −8 dB. Outstanding performance proves the superiority and robustness of the proposed method.

Methods and algorithms for classifying air targets by importance based on data from an airborne radar system Part 1. Two-stage ranking of air targets according to the degree of danger during the operation of radar in the multipurpose tracking mode

Classification of air targets by rank of importance is the basis for ensuring situational awareness of pilots (crew members) of military aircraft, therefore, the problem of increasing the reliability of target classification is very urgent. The requirement to provide the pilot (crew) with full situational awareness means providing him with complete and reliable information about tactical, electronic, navigation situations and the technical condition of on-board systems. It should be noted that when classifying air targets according to the rank of importance, dangerous, favorable for attack and non-dangerous targets are currently usually distinguished. Specific tasks of classifying air targets by importance rank belong to the class of object recognition tasks. In modern aviation radio-electronic complexes, the classification of air targets according to the rank of importance is usually carried out according to the data of on-board radar systems, as well as electronic intelligence systems and optoelectronic systems. It should be noted that one of the main information modes of functioning of modern and future radar systems is the multipurpose tracking mode, in which an airborne radar can simultaneously track a large number of targets in its area of responsibility. In the conduct of hostilities, ensuring one's own security is a priority task for aircraft of various purposes. In this case, the role of dangerous air targets can be not only targets belonging to the opposing side, but also their own aircraft in dangerous encounters, in which a collision can occur between them. Therefore, the problem of preventing dangerous encounters and preventing aircraft collisions with each other becomes one of the key problems in group operations of military aviation. It should be noted that by now there are various methods of assessing goals according to the degree of their danger, but there is no systematic presentation of them. An attempt is made in the article to give a systematized presentation of new methods for solving this problem, based on a two-stage decision-making on the predicted minimum distance of closest approach and the time to reach it. Based on this approach, the following are considered: the method of subjective reduction of private indicators; procedures for determining the hazard indicators of air targets in the antenna coordinate system; rules for making decisions on the degree of danger of targets, taking into account their possible maneuver; – the composition of information support for solving this problem has been determined.

SWaP Optimised Parameter Extraction of Radar Signals for Space Electronic Intelligence Application

Space-based electronic intelligence system provides wide coverage and unrestricted access to adversary radar signals. These systems play a vital role in strategic intelligence gathering for assessing electronic order of battle. These systems need to be SWaP optimized with highly efficient algorithms to extract accurate radar parameters. The realization of such a system is a persistent challenge due to the limited availability of space graded components and associated tools. Towards this, the paper deliberates upon various signal processing algorithms to achieve highly accurate direction-of-arrival (DOA), high-frequency resolution and precise timing information for pulse width and pulse repetition frequency extraction. All the proposed algorithms have been implemented, ported and tested on Xilinx Kintex Ultra Scale FPGA KU060 and being evaluated in the radiation setups to establish the performance. High DOA accuracy and frequency accuracy of the order of 0.3 degree and 0.64 MHz respectively have been achieved.

High Accuracy Parameter Estimation for Advanced Radar Identification of Electronic Intelligence System

Radar identification is one of the vital operations in an electronic intelligence system. The conventional methods based on basic parameters comparison of unique identification of a radar in a cluster of similar radars, is prone to ambiguities. To meet the current tactical requirements of unique identification of a radar, the methodology needs to be based on better feature extraction, even in low SNR conditions. The paper explores a novel technique based on moving autocorrelation for the extraction of intra-pulse and inter-pulse radar parameters. Extensive simulation and empirical studies have been carried out to establish the approach to extend accurate radar parameters in noisy and low SNR conditions. The technique is found to be promising even in field data conditions. The paper describes the methodology, simulation results, FPGA implementation using system generator and resource utilisation summary.

UAV Mission Planning with SAR Application.

The paper presents the concept of mission planning for a short-range tactical class Unmanned Aerial Vehicle (UAV) that recognizes targets using the sensors it has been equipped with. Tasks carried out by such systems are mainly associated with aerial reconnaissance employing Electro Optical (EO)/Near Infra-Red (NIR) heads, Synthetic Aperture Radar (SAR), and Electronic Intelligence (ELINT) systems. UAVs of this class are most often used in NATO armies to support artillery actions, etc. The key task, carried out during their activities, is to plan a reconnaissance mission in which the flight route will be determined that optimally uses the sensors’ capabilities. The paper describes the scenario of determining the mission plan and, in particular, the UAV flight routes to which the recognition targets are assigned. The problem was decomposed into several subproblems: assigning reconnaissance tasks to UAVs with choosing the reconnaissance sensors and designating an initial UAV flight plan. The last step is planning a detailed flight route taking into account the time constraints imposed on recognition and the characteristics of the reconnaissance sensors. The final step is to generate the real UAV flight trajectory based on its technical parameters. The algorithm for determining exact flight routes for the indicated reconnaissance purposes was also discussed, taking into account the presence of enemy troops and available air corridors. The task scheduling algorithm—Vehicle Route Planning with Time Window (VRPTW)—using time windows is formulated in the form of the Mixed Integer Linear Problem (MILP). The MILP formulation was used to solve the UAV flight route planning task. The algorithm can be used both when planning individual UAV missions and UAV groups cooperating together. The approach presented is a practical way of establishing mission plans implemented in real unmanned systems.

LPI Radar Waveform Recognition Based on Features from Multiple Images.

Detecting and classifying the modulation type of the intercepted noisy LPI (low probability of intercept) radar signals in real-time is a necessary survival technique in the electronic intelligence systems. Most radar signals have been designed to have LPI properties; therefore, the LPI radar waveform recognition technique (LWRT) has recently gained increasing attention. In this paper, we propose a multiple feature images joint decision (MFIJD) model with two different feature extraction structures that fully extract the pixel feature to obtain the pre-classification results of each feature image for the non-stationary characteristics of most LPI radar signals. The core technology of this model is combining the short-time autocorrelation feature image, double short-time autocorrelation feature image and the original signal time-frequency image (TFI) simultaneously input into the hybrid model classifier, which is suitable for non-stationary signals, and it has higher universality. We demonstrate the performance of MFIJD by simulating 11 types of the signals defined in this paper and generating training sets and test sets. The comparison with the literature shows that the proposed methods not only has a high universality for LPI radar signals, but also better adapts to LPI radar waveform recognition at low SNR (signal to noise ratio) environment. The overall recognition rate of the method reaches 87.7% when the SNR is −6 dB.

ОЦІНКА ЕФЕКТИВНОСТІ ТА НАДАННЯ ПРАКТИЧНИХ РЕКОМЕНДАЦІЙ ЩОДО ФУНКЦІОНУВАННЯ СИСТЕМИ РАДІОЕЛЕКТРОННОЇ РОЗВІДКИ В ІНТЕРЕСАХ ПІДГОТОВКИ Й ВЕДЕННЯ СТАБІЛІЗАЦІЙНОЇ ОПЕРАЦІЇ

The military-political situation around Ukraine is characterized by high dynamics and instability of events and processes. Against the background of the above-mentioned task of the Armed Forces of Ukraine at the present stage of development is the intensification of intelligence in order to timely warn the top military-political leadership of Ukraine about possible open armed aggression of the Russian Federation, hidden actions of some other neighboring states that may threaten national interests. In the article on the basis of investigated influence of external and internal factors on the efficiency of the operation of the radio-electronic intelligence system in the interests of conducting a stabilization operation by grouping troops (forces) in the territory of Donetsk and Lugansk regions, using methods of analysis, synthesis and theory of probability, the evaluation of the efficiency of the electronic system's functioning, the capabilities of its forces and means to expose changes in the modes of operation of the armed forces, bringing to the highest levels of combat readiness K (forces), active prevention actions of illegal armed groups. The impact of the investigated factors on the performance evaluation indicated that the objective capabilities of the electronic intelligence system were not fully realized and, in some cases, significantly reduced. The paper evaluated the effectiveness of radio-electronic intelligence in the Joint Forces operation in the interest of preparation and conduct of the stabilization operation, which allowed to determine the direction of further research and to develop practical recommendations that made it possible to improve the efficiency of the electronic intelligence system with minimal cost.