Electronic Reconnaissance System Research Articles

Deception mechanisms of FDA‒AWACS against passive monopulse angle measurements

AbstractAirborne warning and control systems (AWACS) serve as critical command and control centres in air combat operations, making them prime targets for strategic attacks. These enemy attacks typically rely on the accurate determination of AWACS combat positions using different direction‐finding devices. In particular, passive monopulse angle measurement systems locate AWACS by measuring the angles of signals emitted by AWACS radiation sources, thus rendering them vulnerable to attacks. Given the criticality of the airborne radars of AWACS in battle command and control operations, they must function continuously to monitor air and sea targets. Hence, AWACS cannot effectively evade electronic reconnaissance systems through tactics such as radar shutdown. To explore alternative measures, the authors investigate the deception mechanisms of an integrated frequency diverse array AWACS (FDA‒AWACS) against passive monopulse angle measurements. Using an established FDA signal model, the principles underlying two common monopulse angle measurement methods are first outlined. Subsequently, the angle estimation formulae typically used by these monopulse angle measurement systems to interpret received FDA radiation signals are derived. Additionally, the transmit beampatterns, amplitude patterns, and angle measurement deception capabilities of several typical FDAs are examined. Simulation results indicate that the FDA‒AWACS can theoretically deceive passive monopulse angle measurement systems to a certain extent. However, one‐dimensional uniform linear FDAs and other arrays using sinusoidal frequency offsets exhibit limited deception abilities. In contrast, arrays utilising cubic and quartic frequency offset achieve angle measurement errors exceeding 2° in far‐field scenarios.

A Novel Dual-Component Radar-Signal Modulation Recognition Method Based on CNN-ST

Dual-component radar-signal modulation recognition is a challenging yet significant technique for electronic reconnaissance systems. To improve the lower recognition performance and the higher computational costs of the conventional methods, this paper presents a randomly overlapping dual-component radar-signal modulation recognition method based on a convolutional neural network–swin transformer (CNN-ST) under different signal-to-noise ratios (SNRs). To enhance the feature representation ability and decrease the loss of the detailed features of dual-component radar signals under different SNRs, the swin transformer is adopted and integrated into the designed CNN model. An inverted residual structure and lightweight depthwise convolutions are used to maintain the powerful representational ability. The results show that the dual-component radar-signal recognition accuracy of the proposed CNN-ST is up to 82.58% at −8 dB, which shows the better recognition performance of the CNN-ST over others. The dual-component radar-signal recognition accuracies under different SNRs are all more than 88%, which verified the fact that the CNN-ST achieves better recognition accuracy under different SNRs. This work offers essential guidance in enhancing dual-component radar signal recognition under different SNRs and in promoting actual applications.

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.

Radar Signal Recognition Based on Bagging SVM

Radar signal recognition under low signal-to-noise ratio (SNR) conditions is a critical issue in modern electronic reconnaissance systems, which face significant challenges in recognition accuracy due to signal diversity. A novel method for radar signal detection based on the bagging support vector machine (SVM) is proposed in this paper.This method firstly utilizes the Choi–Williams distribution (CWD) and the smooth pseudo Wigner-Ville distribution (SPWVD) to obtain different time–frequency images of radar signals, which effectively leverages CWD’s strong time–frequency aggregation and SPWVD’s robust cross-interference resistance. Moreover, histograms of oriented gradient (HOG) features are extracted from time–frequency images to train multiple SVM classifiers by bootstrap sampling. Finally, the performance of each SVM classifier is aggregated using plurality voting to reduce the risk of model overfitting and improve recognition accuracy. We evaluate the effectiveness of the proposed method using 12 different types of radar signals. The experimental results demonstrate that its overall identification rate reaches around 79% at an SNR of −10 dB, and it improves the recognition rate by 5% compared with a single classifier.

A Novel Radar Signals Sorting Method via Residual Graph Convolutional Network

The dense, complex and variable electromagnetic environment poses a serious challenge to radar signal sorting (RSS) in modern electronic reconnaissance systems. In order to improve RSS performance, this letter proposes a semi-supervised learning framework-based RSS method via a residual graph convolutional network (ResGCN-RSS) to effectively improve the generalization ability of the signal sorting models in small data scenarios. Firstly, the graph structure construction of intercepted radar signals is performed via K-nearest neighbor algorithm. Then, the three-layer ResGCN is designed to adaptively improve the features learning. Finally, RSS can be effectively and efficiently implemented through an end-to-end ResGCN with small labeled graph data of interleaved radar signals. The simulation experimental results show that our proposed method can achieve better average accuracy with little computational cost increasing when the labeled data is very small, compared to some existing methods.

Estimation and Classification of NLFM Signals Based on the Time-Chirp Representation.

A new approach to the estimation and classification of nonlinear frequency modulated (NLFM) signals is presented in the paper. These problems are crucial in electronic reconnaissance systems whose role is to indicate what signals are being received and recognized by the intercepting receiver. NLFM signals offer a variety of useful properties not available for signals with linear frequency modulation (LFM). In particular, NLFM signals can ensure the desired reduction of sidelobes of an autocorrelation (AC) function and desired power spectral density (PSD); therefore, such signals are more frequently used in modern radar and echolocation systems. Due to their nonlinear properties, the discussed signals are difficult to recognize and therefore require sophisticated methods of analysis, estimation and classification. NLFM signals with frequency content varying with time are mainly analyzed by time-frequency algorithms. However, the methods presented in the paper belong to time-chirp domain, which is relatively rarely cited in the literature. It is proposed to use polynomial approximations of nonlinear frequency and phase functions describing signals. This allows for applying the cubic phase function (CPF) as an estimator of phase polynomial coefficients. Originally, the CPF involved only third-order nonlinearities of the phase function. The extension of the CPF using nonuniform sampling is used to analyse the higher order polynomial phase. In this paper, a sixth order polynomial is considered. It is proposed to estimate the instantaneous frequency using a polynomial with coefficients calculated from the coefficients of the phase polynomial obtained by CPF. The determined coefficients also constitute the set of distinctive features for a classification task. The proposed CPF-based classification method was examined for three common NLFM signals and one LFM signal. Two types of neural network classifiers: learning vector quantization (LVQ) and multilayer perceptron (MLP) are considered for such defined classification problem. The performance of both the estimation and classification processes was analyzed using Monte Carlo simulation studies for different SNRs. The results of the simulation research revealed good estimation performance and error-free classification for the SNR range encountered in practical applications.

Research on the Sequential Difference Histogram Failure Principle Applied to the Signal Design of Radio Frequency Stealth Radar

As electronic warfare becomes the core of modern warfare, radio frequency (RF) stealth radar is becoming the focus of modern electronic warfare. The anti-sorting strategy of RF stealth radar is a new effective method of the anti-enemy electronic reconnaissance system. Hence, research on the failure mechanism of sorting algorithms has become the cornerstone of anti-sorting technology. In this paper, the mechanism of algorithm failure is studied because the SDIF algorithm is widely used in engineering practice throughout the whole workflow of the sequential difference histogram (SDIF) algorithm, from the estimation of pulse repetition interval (PRI) destroyed by the algorithm and algorithm analysis to the staggered signal. Firstly, the working steps of the SDIF histogram sorting algorithm are considered. Key steps of signal sorting by the algorithm are analyzed, and the failure principle of the sorting algorithm is proposed. It is pointed out that if the PRI signal center value of the two groups of radars is within the tolerance range of the sorting algorithm, when the two signal center values are not of the same order of magnitude, the difference is more than 10 times, and the signal variation is less than 30% of the center value, the sorting error of the algorithm for the radar signal is at least 25%. The sorting algorithm fails to sort signals. At the same time, for the sorting failure of the staggered signal, the sub-PRI design formula of the staggered signal is proposed, and the staggered signal satisfying the design formula can make the sorting algorithm invalid. Finally, the correctness of the SDIF failure principle is further verified by formula derivation, signal design simulation and experiment. The principle of sorting failure provides theoretical support and foundation for the design of anti-sorting RF stealth signal. The principle of sorting failure makes up for the shortcomings of random signal design and improves the design efficiency of RF stealth signal.

Model-Based Representation and Deinterleaving of Mixed Radar Pulse Sequences With Neural Machine Translation Network

Deinterleaving mixtures of radar pulse sequences is the first and the most vital step for modern electronic reconnaissance systems to intercept and analyze the intentions of noncooperative radars. Currently, these systems are facing great challenges due to the emerging complexity of radar modulations. This article proposes a novel method for deinterleaving mixtures of radar pulse sequences based on the time series characteristics of each source (component pulse sequences). First, the mathematical representations are established to describe the structural characteristics of each source. Then, the deinterleaving problem is formulated as a minimization of a maximum-likelihood cost function that can be solved efficiently through a supervised neural machine translation (NMT) network, i.e., to translate each received pulse in the interleaved pulse sequence to the corresponding source label. A sequence-to-sequence NMT model is proposed to capture the structural relationships among the nonadjacent pulses originated from the same source in the mixtures and assign the corresponding label to each pulse. The proposed method does not require the exact knowledge of each component pulse sequence. The experimental results based on the time of arrival sequence of mixed pulse sequences show that the proposed method outperforms the state-of-the-art deinterleaving methods and achieves satisfactory performance under highly nonideal situations with measuring noise and lost pulse conditions. The proposed method can be directly applied to other fields involving deinterleaving problems and multivariate input conditions.

Unknown radar waveform recognition system via triplet convolution network and support vector machine

We propose an unknown radar waveform recognition system for identifying unknown radar waveforms and classifying known radar waveforms simultaneously, which can be summarized into three stages: a training stage that a triplet convolution network is trained to capture features and a support vector machine is leveraged to classify signals with the assistance of known radar waveforms, a pseudo-testing stage that a proper threshold is chosen to set a boundary between known and unknown radar waveforms, and a testing stage that new signal types are distinguished from conventional types by loading models trained in the training stage and the threshold chosen in the pseudo-testing stage. Furthermore, we need to derive the corresponding central feature vector of each known class in the training stage so as to prepare for computing Pearson correlation coefficient in the pseudo-testing stage and the testing stage. Experiments demonstrate the effectiveness of the proposed system on classifying known signals and identifying unknown signals. In the context of adaptive electronic warfare, this research accomplishes identification of radar signal waveforms including unknown signals for the first time, laying a solid foundation for the progress of electronic reconnaissance system.

Distributed Clustering Method Based on Spatial Information

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

МАТЕМАТИЧНА МОДЕЛЬ ФУНКЦІОНУВАННЯ ПІДСИСТЕМИ РОЗПІЗНАВАННЯ СИСТЕМИ РОЗВІДКИ

The intelligence system is a multilevel recognition system. One of the top priorities of the intelligence system for intelligence objects is the task of recognizing them. For example, in an electronic reconnaissance system, the type of radio and radar station is a sign of determining the operational and tactical affiliation of a radio network or radio system, while determining the type of radio and radio station is one of the tasks of recognition. The recognition process is defined as one of the types of classification. The principle of recognition as a process of information processing is the division of information into a posteriori and a priori. Recognition is the process of comparing the obtained intelligence data on a certain intelligence object (a posteriori information) with the data, which consists in the description of all intelligence objects (a priori information) with the help of intelligence features, to determine the affiliation of the intelligence object. A posteriori data on the object of reconnaissance is compared with a priori information using recognition algorithms. Recognition is used to establish the operational and tactical affiliation of individual electronic means, networks, communication nodes, command posts, as well as to take into account the impact of enemy resistance. Some processes can be both processing tasks, such as systematization of information, data, features. At the moment, in a number of works, attempts are being made to expand the feature space and improve the scientific and methodological apparatus of recognition. These works are aimed at improving system performance, in particular - the probability of correct recognition. In order to determine how much this value has changed, depending on various factors, methods of mathematical modeling of the system, as well as laboratory and field experiments are used. The use of the latter is associated with certain difficulties, especially when it comes to a complex dynamic system of intelligence on the activities of foreign states in space, the means of measurement of which have a wide geography of location and differ in various features of operation. To assess the main indicators of the recognition system and ensure the possibility of optimal choice of means of measuring the parameters of the reconnaissance objects, it is proposed to develop a model of system operation. A mathematical model of the functioning of the recognition system is proposed. Application of the considered model will allow to solve the problems connected with construction of systems of recognition and definition of necessary quantity of signs.

LFM/PRBC Modulation Signal Parameter Estimation

Since pseudo-code phase modulation and carrier frequency modulation composite reconnaissance signals have both the good anti-interference capability and the low probability of interception, they have been widely used in electronic reconnaissance systems. This paper proposes a multi-parameter estimation method for pseudo-random binary phase code (PRBC) and linear frequency modulation (LFM) composite modulation signal in a complex electromagnetic environment. Firstly, this paper uses the square operation to remove the code phase information; secondly, we use the delay correlation and two times frequency estimation to estimate the starting frequency and the frequency modulation rate; finally, the wavelet transform algorithm is utilized to estimate the code rate parameter. Through the simulation analysis and full comparison with other methods, we can prove the performance superiority of this method under low signal-to-noise ratios (SNRs).

A Novel Aggregated Multipath Extreme Gradient Boosting Approach for Radar Emitter Classification

Radar emitter classification (REC) plays an increasingly significant role in the electronic reconnaissance system. Due to there are many unreliable factors in traditional feature-based methods, this article focuses on improving the accuracy and stability of REC based on multipaths of original radar signals. A novel aggregated multipath extreme gradient boosting (AMP-XGBoost) is therefore put forward. Multi-path, including multi-scale, multi-domain transformations and their concatenations, is developed to exploit more information of the original signal, which contributes to providing more distinguishable features. XGBoost is used to automatically extract the features contained in these paths and complete recognition. Finally, multiple paths are sifted and aggregated according to certain weights to obtain a higher accuracy. Experiments are carried out based on signals from 6 different intra-pulse modulation mode radars with the same radio frequency (RF) range. It is demonstrated that the proposed method has higher REC accuracy than the methods only based on a single scale or domain. In the meantime, experimental results under different low SNRs show that the proposed method has higher stability. Finally, experiments based on the measured aviation radar data proves the superiority of the proposed method.

An Analysis Method for Solving Ambiguity in Direction Finding with Phase Interferometers

The phase interferometer is an effective direction finding (DF) method. It is widely utilized in various electronic reconnaissance systems due to its advantages of fast operation and high precision. In a wideband system, the combination of long and short baselines, or even multi-level baselines, is applied in an interferometer to solve the contradiction between accuracy and phase ambiguity for DF. In this paper, a novel analysis method is proposed to obtain the probability of successfully solving ambiguity based on mathematical statistics. According to the length ratio between short and long baselines, the joint density function of phase errors can be derived. Then, the probability can be achieved under different signal-to-noise ratios (SNRs) by integrating the joint density function in a specific interval. Furthermore, the formula of phase measurement error is adjusted by the least square method to improve computational accuracy in low SNR during the process. Under different baseline configurations, the strategy can provide the theoretical probability of successfully solving ambiguity, thus guiding the baseline design for obtaining a maximum probability without impacting the specified DF accuracy. Simulation results show that the mathematical model is efficient in some complex cases.

Методический подход к интегральной оценке эффективности применения авиационных комплексов с БПЛА. Часть 1. Методики оценки эффективности решения задач радиосвязи и дистанционного мониторинга

Currently, the use of aviation systems with unmanned aerial vehicles (UAVs) of various classes and special purposes has gained particular relevance and practical importance in the interests of both special customers and civilians. Modern UAVs are used both individually and as part of a group, they can carry several target loads on board simultaneously, they are built on various physical principles: a multifunctional optoelectronic system, a digital aerial system, an airborne radar station, a radio and electronic reconnaissance system, and a system communications for transmitting data from target loads (sensors) to a mobile device (for example, a tablet) to a remote subscriber, etc. However, the question of determining the effectiveness of solving both individual UAV targets and finding an integrated assessment of the effectiveness of using aviation complexes with UAVs when solving a set of targets (sequentially or sequentially-in parallel in time) taking into account their priority and a number of other factors. This article structurally consists of three parts. In the first part, we can observe a scientific and methodological approach to determining the effectiveness of solving particular communication targets and remote monitoring by a complex with UAVs according to the probability of their solution is developed. In the framework of this approach, a mathematical apparatus has been developed for the functional depend-ence of the probabilities of solving particular target problems with the design parameters of target loads as part of the UAV, taking into account the peculiarities of its functioning and under the conditions of existing limitations and assumptions. The scientific and methodological approach allows already at the stage of the formation of the tactical and technical task for the complex with UAVs to obtain, using the calculation method, quantitative estimates of the probabilities of solving particular targets taking into account the technical backlog of industrial enterprises for key components of the complex (target loads, communications equipment complex, etc.). The methodological apparatus developed in the article is universal and invariant with respect to input parameters, i.e., the number of tasks to be solved, the stages of operation of the complex with UAVs, and can be easily adapted to new conditions of use. It should be noted that the result of the article is the methodological apparatus for finding exactly the integral estimate. Finding performance assessments for the group use of UAVs, as well as taking into account possible countermeasures, is beyond the scope of this article and is a direction of further research on this topic.

Effects of Plasma Sheath on Parameter Estimations of Linear Frequency Modulation Pulse Signal

A space vehicle traveling in the atmosphere at hypersonic speed is enveloped by a time-varying plasma sheath, and the performance of onboard radar reconnaissance system will be significantly interrupted. Plasma sheath would generate parasitic modulations of the amplitude and phase of radar signals. Therefore, based on the transmission coefficient of radar wave in time-varying plasma sheath, the Wigner–Hough transform and autocorrelation time-domain signal detection method are used to estimate the frequency- and time-domain parameters of the linear frequency modulation (LFM) pulse signal statistically. A variation rule of the estimation errors of signal parameters is established under various plasma characteristics parameter and frequency and bandwidth of LFM waves. The critical signal-to-noise ratio that is the threshold value for failure of signal parameter estimation has been accurately obtained for different electron densities and radar frequencies. The calculation results could provide useful information for onboard electronic reconnaissance system in further applications.

Deep representation method for radar emitter signal using wavelet packets decomposition

Signal representation is to transform the intercepted original signal into feature parameters, which is the basis for the electronic reconnaissance system to analyse and identify a radar. The traditional method is to manually design a limited number of features with prior knowledge, which can only achieve the desired results in specific applications. Therefore, it is necessary to study signal representation methods that are more adaptable, reasonable, and effective. This study proposes a feature-level design scheme that extracts multi-level signal feature parameters to obtain deep essential feature representations, which is consistent with deep-learning frameworks. Through the decomposition through wavelet packet transform (WPT), the best sub-band tree structure of the signal can be obtained, and the depth representation of the signal can be well achieved, which can meet requirements of time–frequency analysis automatically. Here, Symlets wavelet packet decomposition (WPD) is used as an example to implement multi-layer signal feature representation. The combination of characteristic parameters at different levels can reflect the different information contained in different radar signals. The simulation results show that the method can effectively identify the modulation mode of radar signals and enhance the information mining ability from radar signals.

Radar Signal Parameters Estimation Using Phase Accelerogram in the Time-Frequency Domain

Radar signal parameter estimation, in the context of the reconstruction of the received signal in a passive radar utilizing other radars as a source of illumination, is one of the fundamental steps in the signal processing chain in such a device. The task is also a crucial one in electronic reconnaissance systems, e.g., electronic intelligence systems. In order to obtain accurate results it is important to measure, estimate and precisely reconstruct the considered signal. Then it is necessary to identify it. The following paper presents a new approach to the estimation of the phase parameters of radar pulses. The novel method of chirp-rate estimation is described and used to analyze such signals. First, the method was examined by simulated pulses, and then true measured signals collected from the air traffic control radar were utilized. Using the phase accelerogram, an instantaneous chirp-rate was computed thanks to three novel estimators and the results were compared to results acquired using a different, already known, and estimation method of the phase coefficients based on matching filtration– extended generalized chirp transform.

LPI Radar Waveform Recognition Based on CNN and TPOT

The electronic reconnaissance system is the operational guarantee and premise of electronic warfare. It is an important tool for intercepting radar signals and providing intelligence support for sensing the battlefield situation. In this paper, a radar waveform automatic identification system for detecting, tracking and locating low probability interception (LPI) radar is studied. The recognition system can recognize 12 different radar waveform: binary phase shift keying (Barker codes modulation), linear frequency modulation (LFM), Costas codes, polytime codes (T1, T2, T3, and T4), and polyphase codes (comprising Frank, P1, P2, P3 and P4). First, the system performs time–frequency transform on the LPI radar signal to obtain a two-dimensional time–frequency image. Then, the time–frequency image is preprocessed (binarization and size conversion). The preprocessed time–frequency image is then sent to the convolutional neural network (CNN) for training. After the training is completed, the features of the fully connected layer are extracted. Finally, the feature is sent to the tree structure-based machine learning process optimization (TPOT) classifier to realize offline training and online recognition. The experimental results show that the overall recognition rate of the system reaches 94.42% when the signal-to-noise ratio (SNR) is −4 dB.

Design and Development of FPGA-based High-Performance Radar Data Stream Mining System

Radar signal sorting is a key technique in electronic reconnaissance systems and is currently an important research direction in radar signal processing. Clustering, one of data mining techniques, has been adopted to solve radar sorting problem. However, none of the existing clustering-based methods is able to perform real-time analysis on high speed radar stream. Therefore, in this paper, we propose, design and implement a high performance FPGA-based data stream mining system to perform real-time radar signal sorting on continuous data stream. Firstly, a density-based clustering algorithm is proposed for radar signal sorting; secondly, FPGA-based program of the clustering algorithm is designed and implemented; thirdly, the FPGA board is designed and implemented. Experiments are performed on the board we designed. The results show that the proposed system can achieve real-time radar signal sorting on FPGA, and the resource consumption on FPGA is very low. The clustering algorithm is efficient in terms of accuracy.