Modelling and Optimisation of UHF band EW Based WTA Problem within the Scope of Threat Assessment

The classical weapon target allocation (WTA) problem has been evaluated within the scope of electronic warfare (EW) threat assessment with an electromagnetic effect-based jammer- tactical radio engagement approach. As different from the literature, optimum allocation of non-directional jammers operating at different operating UHF frequencies under constraints to RF emitters is aimed in this study. The values of the targets are modelled using an original threat assessment algorithm developed that takes into account operating frequencies, jamming distance, and weather conditions. The computed jammer-target effect matrix has been solved under different scenarios according to the efficiency and cost constraints. It is seen at the end of the simulations that the allocation results for EW applications largely depend on the effect ratio used. The better results are taken in the case of under 0.5 effect ratio. Finally, jammer-radio allocation problem specified at the suggested model is solved successfully and effectively.

Information Processing Methods of Electronic Warfare Events Based on Communication Technology

Traditional electronic countermeasure incident intelligence processing has problems such as low accuracy and stability and long processing time. A method of electronic countermeasure incident intelligence processing based on communication technology is proposed. First, use the integrated digital signal receiver to identify various modulation methods in the complex signal environment to facilitate the processing and transmission of communication signals, then establish an electronic countermeasure intelligence processing framework with Esper as the core, and flow the situation to the processing conclusion through the PROTOBUF interactive format Redis cache. The data can realize the intelligent processing of electronic countermeasure incidents. The experimental results show that the method proposed in this paper increases the recall rate by 5 to 20% compared with other methods. This method has high accuracy and stability for electronic countermeasure incident intelligence processing and can effectively shorten the time for electronic countermeasure incident intelligence processing.

Ukraine’s integration into the world arms market

Hostilities in eastern Ukraine have highlighted the need for accelerated transformation of Ukrainian armed forces and strengthening country’s defense capabilities. Ukraine is an active consumer and exporter of weapons, so it needs to improve constantly its position in the international arms market, which, by the way, unlike most markets suffering from the COVID-19 pandemic, is constantly growing. The study aims to assess the prospects of Ukraine’s integration into the world arms market to secure its armed forces and achieve the highest possible level of arms exports. Methods of comparative analysis and integrated approach were used in the study. The obtained results demonstrated that Ukrainian arms export has fallen dramatically in recent years (from 1501 million USD in 2012 to 115 million USD in 2020); the largest importers were China (36% of Ukraine’s total arms exports), Russia (20%), and Thailand (17%). The main explanation for this is the necessity of transition to more technological weapons. Concerning weapon imports, Turkey, the USA, Great Britain, Poland, and Bulgaria became the main suppliers. It is about ammunition, electronics, including electronic warfare stations, means of communication, sniper rifles, grenade launchers, etc. The study substantiated that Ukraine should implement joint projects with other countries to fully develop its high-tech weapons in accordance with the latest military technologies.

Specific Radar Recognition Based on Characteristics of Emitted Radio Waveforms Using Convolutional Neural Networks

With the increasing complexity of the electromagnetic environment and continuous development of radar technology we can expect a large number of modern radars using agile waveforms to appear on the battlefield in the near future. Effectively identifying these radar signals in electronic warfare systems only by relying on traditional recognition models poses a serious challenge. In response to the above problem, this paper proposes a recognition method of emitted radar signals with agile waveforms based on the convolutional neural network (CNN). These signals are measured in the electronic recognition receivers and processed into digital data, after which they undergo recognition. The implementation of this system is presented in a simulation environment with the help of a signal generator that has the ability to make changes in signal signatures earlier recognized and written in the emitter database. This article contains a description of the software’s components, learning subsystem and signal generator. The problem of teaching neural networks with the use of the graphics processing units and the way of choosing the learning coefficients are also outlined. The correctness of the CNN operation was tested using a simulation environment that verified the operation’s effectiveness in a noisy environment and in conditions where many radar signals that interfere with each other are present. The effectiveness results of the applied solutions and the possibilities of developing the method of learning and processing algorithms are presented by means of tables and appropriate figures. The experimental results demonstrate that the proposed method can effectively solve the problem of recognizing raw radar signals with agile time waveforms, and achieve correct probability of recognition at the level of 92–99%.

On the accuracy of emitter localization method based on multipath exploitation in realistic scenarios

This study aims to evaluate the accuracy of a method proposed for passive localization of radar emitters around irregular terrains with a single receiver in Electronic Support Measures (ESM) systems. Previously, only the theoretical development of the localization method was targeted by the authors. In fact, this could be a serious concern in practice since there is no evidence about its accuracy under the real data gathered from realistic scenarios. Therefore, firstly, an accurate ray-tracing algorithm is adapted to the method in order to enable its implementation in practice. Then, scenarios are determined based on the geographic information system (GIS) map generated to collect high resolution digital terrain elevation data (DTED) as well as realistic localization problems for radar emitters. Next, the improved method is tested with simulations, and thus, its performance is verified for practical implementation in Electronic Warfare (EW) context for the first time in the literature. Lastly, based on the simulation results, the performance bounds of the method are also discussed.

Estimation of intrapulse modulation parameters of LPI radar under noisy conditions

Low probability of intercept (LPI) radars utilize specially designed waveforms for intra-pulse modulation and hence LPI radars cannot be easily intercepted by passive receivers. The waveforms include linear frequency modulation, nonlinear frequency modulation, polyphase, and polytime codes. The advantages of LPI radar are wide bandwidth, frequency variability, low power, and the ability to hide their emissions. On the other hand, the main purpose of intercept receiver is to classify and estimate the parameters of the waveforms even when the signals are contaminated with noise. Precise measurement of the parameters will provide necessary information about a threat to the radar so that the electronic attack or electronic warfare support system could take instantaneous counter action against the enemy. In this work, noisy polyphase and polytime coded waveforms are analyzed using cyclostationary (CS) algorithm. To improve the signal quality, the noisy signal is pre-processed using two types of denoising filters. The denoised signal is analyzed using CS techniques and the coefficients of spectral correlation density are computed. With this method, modulation parameters of nine types of waveforms up to −12 dB signal-to-noise ratio with an accuracy of better than 95% are extracted. When compared with literature values, it is found that the results are superior.

A Specific Emitter Identification Algorithm under Zero Sample Condition Based on Metric Learning

With the development of information technology in modern military confrontation, specific emitter identification has become a hot and difficult topic in the field of electronic warfare, especially in the field of electronic reconnaissance. Specific emitter identification requires a historical reconnaissance signal as the matching template. In order to avoid being intercepted by enemy electronic reconnaissance equipment, modern radar often has multiple sets of working parameters, such as pulse width and signal bandwidth, which change when performing different tasks and training. At this time, the collected fingerprint features cannot fully match the fingerprint template in the radar database, making the traditional specific emitter identification algorithm ineffective. Therefore, when the working parameters of enemy radar change, that is, when there is no such variable working parameter signal template in our radar database, it is a bottleneck problem in the current electronic reconnaissance field to realize the specific emitter identification. In order to solve this problem, this paper proposes a network model based on metric learning. By learning deep fingerprint features and learning a deep nonlinear metric between different sample signals, the same individual sample signals under different working parameters can be associated. Even if there are no samples under a certain kind of working parameter signal, it can still be associated with the original individual through this network model, so as to achieve the purpose of specific emitter identification. As opposed to the situation in which the traditional specific emitter identification algorithm cannot be associated with the original individual when the signal samples of changing working parameters are not collected, the algorithm proposed in this paper can better solve the problem of changing working parameters and zero samples.

On the S-25 anti-aircraft missile system. Part 1

The paper introduces and analyzes the results of research into the development of anti-aircraft missile systems through the example of the first Russian anti-aircraft missile system S-25. The study describes the capabilities of the anti-aircraft missile system as the implementation of the requirements specifications in the context of the development of military aviation technology and electronic warfare means and emphasizes the importance of cooperation between enterprises — developers of guided missile weapons. Although the latter issue is poorly covered in the Russian scientific, technical, and educational literature, it is an important problem in organizing the development of missile systems. The paper is the first to provide detailed data on the content and timing of the stages of modernization of the S-25 system, and also presents graphic material that clearly demonstrates the change in the capabilities of the S-25 system due to an intensive change in the characteristics of military aircraft.

Recognition method of radar intra-pulse modulation type based on signal square spectrum bandwidth ratio

The twenty-first century is the era of electronic warfare and information warfare. The focus is of the battle between all parties. CEEMD can link the time domain and frequency domain, describe the two-dimensional time–frequency characteristics of the signal, and draw the time–frequency diagram of the signal, so as to reduce the noise signal and improve the signal-to-noise ratio of the signal. The purpose of this paper was to study how to adjust the signal square spectrum bandwidth ratio in the subject of identifying the intra-pulse modulation of radar, so as to solve the problem of identifying the type of radar intra-pulse modulation. The experimental results in this paper show that the decomposition result of EEMD is incomplete and the signal reconstruction error is larger. Compared with the previous two methods, not only the CEEMD method can effectively suppress modal aliasing, but also the decomposition result is complete; the signal reconstruction error is very small, and the decomposition results close to ideal value. The interleaving filter with a bandwidth ratio of 1:2 can divide the 100 GHz channel spacing into asymmetric output spectra with bandwidths greater than 60 GHz and 30 GHz, which effectively improves the current mix of 10 Gb/s and 40 Gb/s The bandwidth utilization of the system illustrates the success of the simulation experiment.