Electronic Reconnaissance Research Articles

The Background and Significance of Radar Signal Sorting Research in Modern Warfare

As an important part of electronic intelligence (ELINT) and electronic support measure (ESM) system, radar signal sorting affects the performance of electronic reconnaissance equipments directly, and is a key technology to campaign decision-making. However, as the radar systems are becoming more and more complicated, as the countermeasure activities in modern electronic warfare are becoming more and more drastic, and as the development of low-probability of intercept (LPI), the regularities of signal sorting are heavily destroyed. This leads to the results of radar signal sorting based on common five parameters are not well. At present, the deinterleaving of advanced radar signals has become the primary bottleneck of signal processing in ELINT and ESM system, and confined farther improvement of the performance of electronic countermeasure equipment. So this paper analyzes and studies the background and significance of radar signal sorting in complex systems.

Fusion Image Based Radar Signal Feature Extraction and Modulation Recognition

The development of cognitive radio and radar electronic reconnaissance has put forward an important demand for improving the recognition ability of modulated signals in complex electromagnetic environment. In this paper, we propose a valid radar signal modulation recognition technology under low signal-to-noise ratio (SNR). The recognition technology can recognize 12 different modulation signals, including Costas, LFM, NLFM, BPSK, P1–P4, and T1–T4 codes. First, we propose the image fusion algorithm of non-multi-scale decomposition to fuse images of a single signal with different time–frequency (T–F) methods. Specifically, weights are designed by the principal component analysis, which could combine significative details of T–F images. Second, we adopt transfer learning-based convolutional neural networks and self-training-based stacked autoencoder, which extract the effective information on fusion image, furthermore guarantee the recognition performance. Moreover, multi-feature fusion algorithm is used to fuse features, which reduces redundant information on features and enhances computing efficiency. Finally, the classifier is performed by a classical algorithm called support vector machine. Simulation results show that the average recognition success rate is 95.5% at SNR of −6dB. It is testified that proposed recognition technology possesses good robustness and superiority in RSR with a wide range of SNR.

A Radar Signal Recognition System Based on Non-Negative Matrix Factorization Network and Improved Artificial Bee Colony Algorithm

The development of cognitive radio and electronic warfare brings new challenges to radar electronic reconnaissance, the recognition of radar signal plays an extreme important role in radar electronic reconnaissance. In order to realize the reliable recognition of radar signal at the condition of low signal-to-noise ratio (SNR), we propose a new radar signal recognition system based on non-negative matrix factorization network (NMFN) and ensemble learning, which can recognize radar signals including BPSK, LFM, NLFM, COSTAS, FRANK, P1, P2, P3 and P4. First, we explore feature extractor based on convolutional neural network (CNN), which applies transfer learning to solve the problem of small sample size. Second, we propose non-negative matrix factorization network to extract features, which can reduce the redundant information. Third, we develop feature fusion algorithm based on stacked autoencoder (SAE), which can acquire essential expression of features and reduce dimension of features. Finally, we propose improved artificial bee colony algorithm (IABC) as the strategy of ensemble learning, which can improve the recognition rate. The simulation results show that the recognition rates reach 94.23% at −4 dB, 99.82% at 6 dB.

Troposcatter Deviation Losses Study for OTH Microwave Propagation

Microwave over-the-horizon propagation via troposcatter mechanism is a valuable candidate for passive sensing of remote radiation source applications, e.g., electronic reconnaissance. The result from the misalignment between target emitting and the system receiving antenna, the additional deviation losses in azimuthal and elevation angle are indispensable to model the receiving antenna pattern as well as the arrival scattering signal level. Based on the scatter transfer function, the deviation losses are derived assuming Gaussian antenna patterns and verified by existing measured data from the 1950s. Finally, potential application values of the deviation losses in electronic reconnaissance are provided.

A New Radar Signal Recognition Method Based on Optimal Classification Atom and IDCQGA

Radar electronic reconnaissance is an important part of modern and future electronic warfare systems and is the primary method to obtain non-cooperative intelligence information. As the task requirement of radar electronic reconnaissance, it is necessary to identify the non-cooperative signals from the mixed signals. However, with the complexity of battlefield electromagnetic environment, the performance of traditional recognition system is seriously affected. In this paper, a new recognition method based on optimal classification atom and improved double chains quantum genetic algorithm (IDCQGA) is researched, optimal classification atom is a new feature for radar signal recognition, IDCQGA with symmetric coding performance can be applied to the global optimization algorithm. The main contributions of this paper are as follows: Firstly, in order to measure the difference of multi-class signals, signal separation degree based on distance criterion is proposed and established according to the inter-class separability and intra-class aggregation of the signals. Then, an IDCQGA is proposed to select the best atom for classification under the constraint of distance criterion, and the inner product of the signal and the best atom for classification is taken as the eigenvector. Finally, the extreme learning machine (ELM) is introduced as classifier to complete the recognition of signals. Simulation results show that the proposed method can improve the recognition rate of multi-class signals and has better processing ability for overlapping eigenvector parameters.

Design and research of signal-level airborne electronic reconnaissance simulation system

Airborne passive electronic reconnaissance equipment has developed rapidly during recent years. However, because of its expensive and unavailable military features, the simulation realization of these equipment needs to be solved. And the smaller the simulation particle is, the better the simulation system will be. In this study, a signal-level airborne electronic reconnaissance simulation system was built. Mathematical model and simulation realization of each part are introduced in this study. Focusing on the shortness of traditional signal sorting methods, we creatively proposed a presorting method based on the Euclidean distance inside signal flows. Simulation results show that the presorting method based on Euclidean distance successfully reduced the pressure on main sorting and appeared good for signal sorting. Simulation test results of each model built in this study are also shown in the study. This study provides a new thought on the realization of airborne electronic reconnaissance equipment and benefits the development of electronic countermeasure.

Specific ship detection for electronic reconnaissance data based on clustering and NNs

Ship detection is an important issue in both civil and military fields. There are many researches about ship detection in wide sea areas and inshore areas. Most of these researches use the image dataset. In this research, a novel framework for specific ship detection using the electronic reconnaissance dataset is proposed. In the new framework, the clustering methods based on time and trajectory are used to process the dataset. Then, several machine-learning methods are used to build detection models. The long short-term memory models are used to extract time-series features and the carefully designed one-dimensional (1D) convolution neural networks (CNNs) are introduced to extract local features. Experimental results on a large electronic reconnaissance dataset collected from real scenario show the model based on 1D CNN gets better performance than classic detection models and the authors' system achieves a good detection accuracy of 92.5%. Above all, this research is a very valuable exploration for the detection of specific ship based on electronic reconnaissance dataset.

Wideband dual-polarized Vivaldi antenna with improved balun feed

AbstractThe paper researches a kind of wideband dual-polarized Vivaldi antenna with improved balun feed which consists of slot line and two bent coplanar strip lines. The whole of antenna structure is composed of two orthogonal Vivaldi antenna elements, and the mode of feeding adopts electromagnetic coupling from micro-strip line to slot line. The improved balun can avoid the intersection of transmission lines, reduce the size, and simplify the assembly. The electromagnetic simulation and optimization design of proposed antenna are carried out by using electromagnetic simulation software, CST. Simulation results show that the isolation between two polarized ports is more than 15 dB within working frequency range of 2–3.5 GHz and VSWRs of two ports are lower than 2.5 at the range of 2–3.5 GHz. The designed antenna is fabricated and measured. The measured results indicate that the designed antenna achieves anticipated patterns and feasible design of the dual-polarized Vivaldi antenna. The dual-polarized Vivaldi antenna with improved balun feed is suitable for some application fields, such as passive electronic reconnaissance, passive radar, and wideband communication and detection.

Математична модель функціонування органів радіолокаційної розвідки РВіА

The experience of modern wars and military exercises testifies that 70-75% of the total volume of enemy fire destruction tasks in the operation is assigned to missile forces and artillery (MFA), and in the conditions of conducting an antiterrorist operation in the territories of Donetsk and Lugansk regions (ATO) this the indicator reached 90-95%. The fulfillment of such a large volume of fire missions depends on the effectiveness of combat support for the combat operations of the MFA.Intelligence information is obtained by performing reconnaissance missions, for which reconnaissance units are created from the artillery reconnaissance (AR) units and additional forces and assets.The experience of the use of reconnaissance units in the ATO testifies that the greatest losses were incurred by the means of radar reconnaissance (RR), which were detected by radio electronic radiation with the means of electronic reconnaissance of the enemy. This led to constant bombardment of the positions of the radar with enemy artillery and to the cases of their defeat.One way to increase the survivability of the means of RR is to reduce the time of radio electronic radiation while conducting reconnaissance and maneuvering between positions.Thus, in the practice of using the means of RR, a contradiction arose between the requirement to improve the quality of reconnaissance information about enemy facilities in the interests of their fire damage to the MFA and the requirement to ensure the required level of survivability of the means of RR. Time of electronic radiation, on the one hand, should be increased to ensure the quality of information, and, on the other hand, it should be reduced to ensure survivability.The purpose of the article is to improve the model of the operation of RR tools, which will enable to determine the values of their operation indicators for further substantiation of recommendations on the order of conducting reconnaissance with ensuring the required survivability index.The existing model has been further developed by including in the graph of functional states of the means of RR: the most probable conditions for the stay of the means of RR in obtaining reconnaissance information about enemy targets; transition from the state “under the fire influence of the enemy” to the “change position” state. This transition describes the process of implementing the release of the means of RR from under the enemy's fire to ensure vitality and create more favorable conditions for further reconnaissance.The model of the operation of the RR facilities makes it possible to determine the indices of the process of their functioning in the conditions of combat operations and in the future to justify recommendations on the order of conducting reconnaissance in order to increase their survivability.

Mixing Matrix Estimation of Underdetermined Blind Source Separation Based on Data Field and Improved FCM Clustering

In modern electronic warfare, multiple input multiple output (MIMO) radar has become an important tool for electronic reconnaissance and intelligence transmission because of its anti-stealth, high resolution, low intercept and anti-destruction characteristics. As a common MIMO radar signal, discrete frequency coding waveform (DFCW) has a serious overlap of both time and frequency, so it cannot be directly used in the current radar signal separation problems. The existing fuzzy clustering algorithms have problems in initial value selection, low convergence rate and local extreme values which will lead to the low accuracy of the mixing matrix estimation. Consequently, a novel mixing matrix estimation algorithm based on data field and improved fuzzy C-means (FCM) clustering is proposed. First of all, the sparsity and linear clustering characteristics of the time–frequency domain MIMO radar signals are enhanced by using the single-source principal value of complex angular detection. Secondly, the data field uses the potential energy information to analyze the particle distribution, thus design a new clustering number selection scheme. Then the particle swarm optimization algorithm is introduced to improve the iterative clustering process of FCM, and finally get the estimated value of the mixing matrix. The simulation results show that the proposed algorithm improves both the estimation accuracy and the robustness of the mixing matrix.

Research Into a Recovery Method of GNSS Authorized Service Signal Component

A code recovery method of a global navigation satellite system (GNSS) authorized service (AS) signal component is described under the condition of a high signal-to-noise ratio. The correctness of the spreading code verification can be used to monitor the health status of satellite payloads. Pseudo code recovery is a precondition to verify the validity of pseudo code. For a specified GNSS signal multiplexing approach, the distribution of baseband signal phase points on a constellation diagram is established. The open service signal component and the AS signal component correlate with each other on the unit circle. By wiping off the signal carrier, each signal component can be recovered according to the phase table of the transmitted signal. Different states of the global positioning system L1 signal is introduced, and the AS signal component recovery performance under different constant enveloping modulations is analyzed. This paper provides a reference for the analysis, evaluation, and system optimization design of a GNSS signal. Relevant research results can be applied to satellite signal quality monitoring and electronic reconnaissance.

Linear polarization to left/right-handed circular polarization conversion using ultrathin planar chiral metamaterials

We propose ultrathin planar chiral metamaterials (CMs) based on square split rings (SSRs), which can change linearly polarized (LP) electromagnetic (EM) wave to circularly polarized (CP) EM wave at will. The EM resonant properties of the proposed CMs including magnitude, phase, retrieved electromagnetic parameters, and chirality are demonstrated. According to the polarization property of the proposed CMs, a CP patch antenna using the proposed CMs is constructed. Placing the proposed CMs in the presence of a conventional LP patch antenna, the antenna polarization mode can be changed from LP mode to CP mode. The antenna performances are investigated numerically and experimentally. A simple method for realizing CP antenna is provided using the present CMs. It can be expected that the proposed CP antenna can be used in electronic reconnaissance and jamming, mobile communication, and global position system.

Research on Mission Scheduling of Electronic Reconnaissance Satellites Based on Multi-area

Regarding to the issue of how electronic reconnaissance satellites (ERS) execute the regional survey mission task. Time, area and frequency are discretization handled in this paper. Based on the analysis of different occasions and task requirements on ERS, multi-Constrained Optimization Model is established to solve relative problems. Through modifying the structure of code, initializing the method and introducing the penalty function, genetic algorithm could apply to the model. Conclusion of the emulation indicates that the ERS area census task could be effectively satisfied by the model and algorithm in this paper.

Distributed emitter parameter refinement based on maximum likelihood

PurposeEmitter parameter estimation via signal sorting is crucial for communication, electronic reconnaissance and radar intelligence analysis. However, due to problems of transmitter circuit, environmental noises and certain unknown interference sources, the estimated emitter parameter measurements are still inaccurate and biased. As a result, it is indispensable to further refine the parameter values. Though the benchmark clustering algorithms are assumed to be capable of inferring the true parameter values by discovering cluster centers, the high computational and communication cost makes them difficult to adapt for distributed learning on massive measurement data. The paper aims to discuss these issues.Design/methodology/approachIn this work, the author brings forward a distributed emitter parameter refinement method based on maximum likelihood. The author’s method is able to infer the underlying true parameter values from the huge measurement data efficiently in a distributed working mode.FindingsExperimental results on a series of synthetic data indicate the effectiveness and efficiency of the author’s method when compared against the benchmark clustering methods.Originality/valueWith the refined parameter values, the complex stochastic parameter patterns could be discovered and the emitters could be identified by merging observations of consistent parameter values together. Actually, the author is in the process of applying her distributed parameter refinement method for PRI parameter pattern discovery and emitter identification. The superior performance ensures its wide application in both civil and military fields.

Cognate pulse sorting method based on beam missions characteristics

In most multi-function phased array radar applications, multiple missions, including airspace searching and target tracking, are usually performed simultaneously by the digital beam-forming technique and the time dividing method. This paper presents a novel method to classify pulses of different missions from an interleaved pulse sequence emitted by the same radar, which is significant in radar electronic reconnaissance and electronic support measure. Firstly, two hypotheses, i.e., pulse relativity within the same mission and pulse independence among different missions, are proposed by analyzing the antenna pattern and the beam scheduling method of the phased array radar. Based on the above two hypotheses, an optimal model for pulse classification is exploited with pulse amplitude series, where the absolute-value sum of second order difference is taken as the optimal kernel to measure sequence smooth continuity. Finally, several pieces of sequences under different numbers of missions and tracking data rates are simulated for algorithm verification. The simulation results show that the long data length and the high data rate will increase classification efficiency due to the validity of the two hypotheses in sufficient pulse amplitude sequence.

A new evaluation model of comprehensive radio frequency stealth performance of radar

The detection capabilities of passive electronic warfare reconnaissance equipment have substantially increased during recent years. Correspondingly, the radar equipment is required to take various means to improve the radio frequency (RF) stealth performance to ensure the transmitted RF signal does not get intercepted. However, traditional evaluation methods on RF stealth performance cannot accurately evaluate the RF stealth capabilities of new system radar. In this study, a joint interception probability evaluation model on RF stealth performance was established, which divided the interception process into two parts: front interception and system interception. Various RF stealth means adopted by different radar equipment were taken into consideration to improve the applicability of this model. Simulation results show that this model is able to effectively characterize almost all the aspects of the RF stealth features and can serve as a good reference to evaluate RF radar stealth performance comprehensively.

Separation of Intercepted Multi-Radar Signals Based on Parameterized Time-Frequency Analysis

AbstractModern radars use complex waveforms to obtain high detection performance and low probabilities of interception and identification. Signals intercepted from multiple radars overlap considerably in both the time and frequency domains and are difficult to separate with primary time parameters. Time–frequency analysis (TFA), as a key signal-processing tool, can provide better insight into the signal than conventional methods. In particular, among the various types of TFA, parameterized time-frequency analysis (PTFA) has shown great potential to investigate the time–frequency features of such non-stationary signals. In this paper, we propose a procedure for PTFA to separate overlapped radar signals; it includes five steps: initiation, parameterized time-frequency analysis, demodulating the signal of interest, adaptive filtering and recovering the signal. The effectiveness of the method was verified with simulated data and an intercepted radar signal received in a microwave laboratory. The results show that the proposed method has good performance and has potential in electronic reconnaissance applications, such as electronic intelligence, electronic warfare support measures, and radar warning.

Electronic warfare system simulation based on SystemVue

Electronic warfare simulation can allow the realistic evaluation of the performance of equipments and techniques, and it can also allow the realistic training of individuals under conditions that, in real life, might kill them. We propose a new scheme of EW simulation system based on SystemVue. This paper mainly expounds the structure of system and the key technology of subsystem establishment. As a proof of concept, a radar electronic reconnaissance simulation experiment is explained in this paper. And the results verify the feasibility of the scheme.

Deception Jamming for Squint SAR Based on Multiple Receivers

A deception jamming method for synthetic aperture radar (SAR) based on multiple receivers is proposed. This method deals with the parameters of hostile SAR as a whole for deception jamming. By measuring the range differences between different receivers, higher accuracy can be achieved than detecting each of the parameters directly. The squint angle, which is hard to be obtained for the electronic reconnaissance, is also included in the parameters the multireceiver system gets, so this method works on the squint SAR. The accuracy of the algorithm and the system noise are analyzed. Simulation results verify the effectiveness of the algorithm.

Incremental Discriminant Analysis on Interval-Valued Parameters for Emitter Identification

Emitter identification has been widely recognized as one crucial issue for communication, electronic reconnaissance, and radar intelligence analysis. However, the measurements of emitter signal parameters typically take the form of uncertain intervals rather than precise values. In addition, the measurements are generally accumulated dynamically and continuously. As a result, one imminent task has become how to carry out discriminant analysis of interval-valued parameters incrementally for emitter identification. Existing machine learning approaches for interval-valued data analysis are unfit for this purpose as they generally assume a uniform distribution and are usually restricted to static data analysis. To address the above problems, we bring forward an incremental discriminant analysis method on interval-valued parameters (IDAIP) for emitter identification. Extensive experiments on both synthetic and real-life data sets have validated the efficiency and effectiveness of our method.