Radar Detection System Research Articles

DESIGN AND DEVELOPMENT OF A STEALTH UNIC FOR SILENT OPERATION WITH ESP32-BASED AUTONOMOUS CONTROL

This research aims to design and build a stealth kamikaze drone for Silent Operation, named Unic, with an autonomous control system. In a modern context that increasingly emphasizes military technology, the existence of drones capable of conducting precision attacks without detection has become essential. This drone is designed to target enemy objectives with the aim of effectively detonating targets without being detected. The design process includes the selection of key components using ESP32, the development of autonomous navigation algorithms, and the integration of secure communication systems. In terms of stealth, the drone is equipped with silent-designed brushless motors that produce noise levels below 25 dB to reduce the likelihood of being heard from a distance. This research also includes performance testing of the drone in simulated scenarios, focusing on maneuverability, navigation accuracy, and stealth capabilities to avoid radar detection and other surveillance systems. Test results show that Unic can perform autonomous flights with high accuracy, optimizing stealth to evade radar and sound detection. These findings are expected to contribute to the development of military drone technology and enhance the effectiveness of national defense strategies. Future research will focus on improving sensor capabilities and anti-jamming systems to support operations in complex environments.

1-Bit Reconfigurable Transmitarray Antenna with Out-of-Band RCS Reduction

Stealth reconfigurable transmitarray antennas (RTAs) are essential components in wireless communication and radar detection systems. Therefore, in this study, we propose a 1-bit RTA with out-of-band radar cross-section (RCS) reduction. The antenna consists of an absorptive frequency selective transmission (AFST) layer and RTA layer separated by air. Specifically, the AFST layer achieves out-of-band RCS reduction and in-band transmission utilizing the first three resonant modes of a bent metallic strip with a centrally loaded resistor. Meanwhile, the RTA layer adopts a receiver–transmitter structure with an active receiving dipole and a passive orthogonal transmitting dipole. 1-bit phase shift is achieved by alternating two pin diodes integrated on the active dipole to reverse its current direction. To evaluate the proposed design, a 16 × 16-element prototype was designed, fabricated, and measured. For scattering, the bandwidth of 10 dB RCS reduction was about 52.5% and 43.8%, respectively. For radiation, the measured gain was 20.1 dBi at 7.5 GHz, corresponding to an aperture efficiency of 12.7%. The gain loss of beam scans to ±60° was about 5 dB in both two principal planes.

Joint radar and communication based on piece-wised LFM signals

Abstract Existing joint radar detection and communication(JDC) systems suffer from high peak-to-average power ratio(PAPR) or distance blind spot zones. In this paper, we present a novel JDC system based on piece-wised linear frequency modulation(LFM) signals. Based on the proposed piece-wised LFM modulation signal, the novel JDC system can achieve linear frequency modulation continuous wave(LFMCW) detection, and the PAPR and blind zone issues are solved. Besides, spread spectrum communication can be simultaneously conducted with the same modulation signal. Simulation results show that the proposed JDC system has similar detection performance to the traditional sawtooth LFMCW detection scheme, and the bit-error rate of communication is less than 10−6 when the the bit rate is 500kbps and the signal-to-noise ratio is 10 dB.

YOLOv8-MDS: A YOLOv8-Based Multi-Distance Scale Drone Detection Network

Abstract Drones have become widely used across various fields, showcasing their capabilities while also raising significant security and privacy concerns. Current detection methods, such as radar, radio frequency, and acoustic detection systems, face issues like high costs and poor interference resistance. The rapid development of computer vision has led to the emergence of visual-based drone detection solutions. To address the challenge of varying drone sizes at different distances impacting visual detection, this paper constructs a multi-distance scale drone dataset with images captured at different distances and environments. Additionally, a Multi-Distance Scale Feature Attention Module (MDS-Module) is proposed and integrated into the neck of the model via leap connections to enhance global feature detection. Furthermore, to account for the typically rectangular nature of drone anchor boxes, the EIoU loss function is used in the detection head to improve the model’s detection capability for drone targets. we conducted comprehensive ablation and comparative experiments on the improved model. The experimental results demonstrate that incorporating the Multi-Distance Scale Feature Attention Module significantly enhances the model’s ability to detect drone targets across multiple distance scales.

Two Omnidirectional Antenna Models for Ship Hull Corrosion Detection Radar System

This paper discusses two modified monopole antenna models. These antenna models are designed to detect hull corrosion of ships, which is predicted to be the cause of hull leaks. The modification was carried out by adding a conical parasitic element aimed at widening the bandwidth and increasing the antenna gain. The conical parasitic on the first antenna is directed upwards, while the second is the opposite. The first model uses a cone with a larger diameter than the second model. The calculation of the cone diameter is based on half the wavelength of the frequencies generated by the antenna. With a smaller diameter, PTFE is added between the monopole and the parasitic cone to avoid short circuits. This configuration is intended to see the effective antenna model widen the bandwidth and increase the gain. Antenna performance testing was carried out using a vector network analyzer and software-defined radio. The test results show that both antenna models are omnidirectional in radiation. The first model operates at a frequency of 3904.7 - 7793.1 MHz with a bandwidth of 3888.4 MHz and a highest gain of 10 dBi. The second model operates at a frequency of 2282.4 - 3324.1 MHz with a bandwidth of 1041.7 MHz and a highest gain of 8dBi. Thus, the first antenna model has a higher bandwidth and gain than the first model, but both have met the requirements for ship hull corrosion detection antennas.

Design of Three-Element Circular Polarization Antenna Array Based on Sequentially Rotated Feed Technique

Circularly polarized antennas are used in communications between ground stations and satellites to achieve reliable communication links. The right-hand circular polarization and left-hand circular polarization are two types of circular polarization in satellite communications, they are used to support uplink and downlink communications. Circularly polarized antennas are used also in radar system for target detection, tracking and identification. The “three-element circularly polarized microstrip array antenna” is designed to produce left-handed circular polarization, make its size compact, make its bandwidth wider than 3.7–4.2GHz and achieve high gain. Circular polarization element antenna and three-element circularly polarized microstrip array antenna are designed and simulated in software HFSS, and the circular polarization element antenna is manufactured and tested in anechoic chamber. For circular polarization element antenna and three-element circularly polarized microstrip array antenna, the study analyzed these parameters: AR, S(1,1), VSWR, bandwidth, normalized impedance, gain and realized gain, radiation efficiency. After optimized, the study get the required results of them.

Multifunctional Metamaterial with Reconfigurable Electromagnetic Scattering Properties for Advanced Stealth and Adaptive Applications.

The rapid development of radar detection systems has led to an increased sensitivity to the electromagnetic (EM) scattering properties of detected targets. Flexible and adaptable EM scattering properties significantly enhance the survivability of battlefield weapons. This paper presents the design of a novel multifunctional metamaterial with reconfigurable EM scattering properties based on a bistable curved beam. In addition to the cushioning and energy absorption properties of curved beams, the metamaterial achieves more than 90% EM absorption in the frequency range of 2.17-17.31GHz, with a relative thickness of only 0.09λL. The bistable nature of the metamaterial allows it to switch between different states. Moreover, combined with the digital coding, this metamaterial can continuously adjust the absorbing bandwidth and further enhance the EM absorption rate within a specific frequency band range. If applied to satellite configurations, the developed metamaterial significantly reduces the radar cross section and offers potential applications in reconfiguring EM scattering properties, when applied to satellite configurations. By actively controller and reconstructing the EM scattering properties at certain frequency points, the metamaterial can achieve camouflage, providing innovative solutions for future stealth technology, electronic countermeasures, and deception jamming in radar detection.

A Vortex Electromagnetic Wave Beam Control Method based on Amplitude-Modulated Concentric Circular Array

Abstract In order to enhance the design efficiency of vortex electromagnetic (EM) wave radar detection systems, a method for generating and controlling vortex EM waves based on amplitude-modulated concentric circular array (CCA) is proposed. This method allows for precise control of the vortex EM waves, ensuring accurate main lobe direction while effectively reducing sidelobes. Based on approximate orthogonal transformation, formulas for calculating the excitation amplitudes of each sub-circular array are derived and the time complexity of this method is negligible compared to search-based algorithms. Extensive simulation experiments are conducted and compared with the results obtained from the genetic algorithm (GA) approach, providing robust evidence of the effectiveness of this method(SLL < -30dB).

Deep-Autoencoder-Based Radar Source Recognition: Addressing Large-Scale Imbalanced Data and Edge Computing Constraints

Radar radiation source recognition technology is vital in electronic countermeasures, electromagnetic control, and air traffic management. Its primary function is to identify radar signals in real time by computing and inferring the parameters of intercepted signals. With the rapid advancement of AI technology, deep learning algorithms have shown promising results in addressing the challenges of radar radiation source recognition. However, significant obstacles remain: the radar radiation source data often exhibit large-scale, unbalanced sample distribution and incomplete sample labeling, resulting in limited training data resources. Additionally, in practical applications, models must be deployed on outdoor edge computing terminals, where the storage and computing capabilities of lightweight embedded systems are limited. This paper focuses on overcoming the constraints posed by data resources and edge computing capabilities to design and deploy large-scale radar radiation source recognition algorithms. Initially, it addresses the issues related to large-scale radar radiation source samples through data analysis, preprocessing, and feature selection, extracting and forming prior knowledge information. Subsequently, a model named RIR-DA (Radar ID Recognition based on Deep Learning Autoencoder) is developed, integrating this prior knowledge. The RIR-DA model successfully identified 96 radar radiation source targets with an accuracy exceeding 95% in a dataset characterized by a highly imbalanced sample distribution. To tackle the challenges of poor migration effects and low computational efficiency on lightweight edge computing platforms, a parallel acceleration scheme based on the embedded microprocessor T4240 is designed. This approach achieved a nearly eightfold increase in computational speed while maintaining the original training performance. Furthermore, an integrated solution for a radar radiation source intelligent detection system combining PC devices and edge devices is preliminarily designed. Experimental results demonstrate that, compared to existing radar radiation source target recognition algorithms, the proposed method offers superior model performance and greater practical extensibility. This research provides an innovative exploratory solution for the industrial application of deep learning models in radar radiation source recognition.

Improved Particle Filter Algorithm for Multi-Target Detection and Tracking.

In modern radar detection systems, the particle filter technique has become one of the core algorithms for real-time target detection and tracking due to its good nonlinear and non-Gaussian system state estimation capability. However, when dealing with complex dynamic scenes, the traditional particle filter algorithm exposes obvious deficiencies. The main expression is that the sample degradation is serious, which leads to a decrease in estimation accuracy. In multi-target states, the algorithm is difficult to effectively distinguish and stably track each target, which increases the difficulty of state estimation. These problems limit the application potential of particle filter technology in multi-target complex environments, and there is an urgent need to develop a more advanced algorithmic framework to enhance its robustness and accuracy in complex scenes. Therefore, this paper proposes an improved particle filter algorithm for multi-target detection and tracking. Firstly, the particles are divided into tracking particles and searching particles. The tracking particles are used to maintain and update the trajectory information of the target, and the searching particles are used to identify and screen out multiple potential targets in the environment, to sufficiently improve the diversity of the particles. Secondly, the density-based spatial clustering of applications with noise is integrated into the resampling phase to improve the efficiency and accuracy of particle replication, so that the algorithm can effectively track multiple targets. Experimental result shows that the proposed algorithm can effectively improve the detection probability, and it has a lower root mean square error (RMSE) and a stronger adaptability to multi-target situation.

Strategy of Russian Military Cooperation with the Countries of the Arab Region

The article undertakes an analysis of the prospective domains of Russian military collaboration with Arab countries against the backdrop of geopolitical uncertainties. Despite maintaining its position as the second-largest arms exporter globally, Russia has experienced a proportional decline in its share of the global arms market. Nevertheless, a discernible uptick in the trade volume of conventional weaponry, coupled with cyclical enhancements in the tactical and technical attributes of military assets, reflects positive momentum in this sector. Amid prevailing external constraints and a noticeable scarcity of contemporary Russian weaponry in burgeoning segments of the arms market, Russia necessitates resolute and equitable measures to uphold and fortify collaborative ties with partner nations. Within this context, particular emphasis is placed on Russia's strategic military cooperation with Arab states, given the region's status as a focal point of geopolitical interests for major economies and a primary destination for arms imports.The article scrutinizes Russia's competitive standing in the arms and military equipment markets of the Arab world, acknowledging the region's significance as a pivotal importer of such goods. Employing a multifaceted evaluation encompassing absolute and relative statistical metrics and derived indices, the study assesses the region's role in global arms procurement. Moreover, it offers an appraisal of the existing state and potential avenues for enhancing the efficacy of the Russian-Arab military-technical collaboration framework. The discourse delves into the transformative prospects of this partnership within the framework of networkcentric warfare concepts. Furthermore, the article elucidates the principal risks inherent in military cooperation among nations at present, highlighting concerns regarding the possible manipulation of military-technical cooperation mechanisms to serve as leverage in furthering the geopolitical aspirations of specific stakeholders.In conclusion, the study posits forward-looking trajectories for bolstering bilateral relations, both sectorally and geographically. These include initiatives such as expanding licensing, leasing, offset transactions, and technology transfer initiatives; advocating for fifth-generation fighter aircraft and long-range radar detection and control systems, leveraging Industry 4.0 technologies; and prioritizing the training of foreign specialists.

Radar Signal Classification with Multi-Frequency Multi-Scale Deformable Convolutional Networks and Attention Mechanisms

In the realm of short-range radar applications, the focus on detecting “low, slow, and small” (LSS) targets has escalated, marking a pivotal aspect of critical area defense. This study pioneers the use of one-dimensional convolutional neural networks (1D-CNNs) for direct slow-time dimension radar feature extraction, sidestepping the complexity tied to frequency and wavelet domain transformations. It innovatively employs a network architecture enriched with multi-frequency multi-scale deformable convolution (MFMSDC) layers for nuanced feature extraction, integrates attention modules to foster comprehensive feature connectivity, and leverages linear operations to curtail overfitting. Through comparative evaluations and ablation studies, our methodology not only simplifies the analytic process but also demonstrates superior classification capabilities. This establishes a new benchmark for efficiently classifying low-altitude entities, such as birds and unmanned aerial vehicles (UAVs), thereby enhancing the precision and operational efficiency of radar detection systems.

Interrupted sampling repeater jamming suppression based on multiple extended complex‐valued convolutional auto‐encoders

AbstractInterrupted sampling repeater jamming (ISRJ) with flexible modulation parameters and coherent processing gain seriously threatens the radar detection system. The jamming suppression and target detection performance of existing anti‐jamming methods are limited by strong noise and jamming signals. An ISRJ suppression method combining multiple extended complex‐valued convolutional auto‐encoders (CVCAEs) and compressed sensing (CS) reconstruction is proposed. For the different tasks of parameter estimation and signal denoising, the extended CVCAEs including a complex‐valued convolutional shrinkage network (CVCSNet) and a complex‐valued UNet (CVUNet) are developed. Based on the time‐domain discontinuity of ISRJ signals, the CVCSNet is first used to directly estimate the parameters representing signal components and extract jamming‐free signals from received signals. Then, the extracted signals are denoised using the CVUNet. After that, relying on the denoised signals and the frequency sparsity of de‐chirped target signals, a CS model is established and solved to recover complete target signals for jamming suppression. Utilising the advantages of deep neural networks in weak feature extraction and signal representation, the CVCSNet and CVUNet can effectively improve the signal extraction accuracy and alleviate the limitation of noise on target signal reconstruction. Experimental results verify that the proposed method has superior ISRJ suppression performance and is robust to varying signal‐to‐noise ratios, jamming‐to‐signal ratios and jamming parameters.

Spatiotemporal features of traffic help reduce automatic accident detection time

Quick and reliable automatic detection of traffic accidents is of paramount importance to save human lives in transportation systems. However, automatically detecting when accidents occur has proven challenging, and minimizing the time to detect accidents (TTDA) by using traditional features in machine learning (ML) classifiers has plateaued. We hypothesize that accidents affect traffic farther from the accident location than previously reported. Therefore, leveraging traffic signatures from neighboring sensors that are adjacent to accidents should help improve their detection. We confirm this hypothesis by using verified ground-truth accident data, traffic data from radar detection system sensors, and light and weather conditions and show that we can minimize the TTDA while maximizing classification performance by considering spatiotemporal features of traffic. Specifically, we compare the performance of different ML classifiers (i.e, logistic regression, random forest, and XGBoost) when controlling for different numbers of neighboring sensors and TTDA horizons. We use data from interstates 75 and 24 in the metropolitan area that surrounds Chattanooga, TN. Our results show that the XGBoost classifier produces the best results by detecting accidents as quickly as 1.0 min after their occurrence with an area under the receiver operating characteristic curve of up to 83% and an average precision of up to 49%. We describe limitations, open challenges, and how the proposed framework can be used for quicker operational accident detection.

Biomedical Radar System for Real-Time Contactless Fall Detection and Indoor Localization

Fall incidents represent a major public health problem among elderly people. This resulted in a significant increase of the number of investigated systems aiming at detecting falls promptly. In this respect, in this work, a biomedical radar system is proposed for remote real-time fall detection and indoor localization. The system, consisting of a sensor and a base station, combines radar and wireless communication techniques, and uses a data processing technique to distinguish between fall events and normal movements. The classification, based on a Least-Square Support Vector Machine (LS -SVM), combined with the sliding window principle allows to perform fall detection in real-time. Moreover, it is capable to localize the subjects when the fall incident has been detected. The in-vivo validation showed a high success rate in detecting fall events, with a maximum delay of 340 ms. Moreover, a maximum mean absolute errors (MAE) of 3.8 cm and a maximum root-mean-square error (RMSE) of 7.5 cm were reported in measuring the subject's absolute distance.

Breakthrough and Practice of Ground Penetrating Radar Detection Technology in Baiyangdian Lake

In order to investigate the stratum structure of the lake bottom in the Baiyangdian Lake area, implement the test of the water-based ground penetrating radar detection system, explore the working mode of the water-based radar detection, compare the water exploration capabilities of different hardware devices, analyze the electromagnetic wave propagation characteristics in the Baiyangdian Lake area, and interpret the geophysical response of the native strata at the bottom of the lake. The stratum structure data in the 15m depth range of the bottom of the lake was obtained to provide a basis for the establishment of a three-dimensional geological model of the Dian District. This technical achievement can strongly promote the construction of a lake and wetland water-land integrated technical method system.

Photonics-assisted joint radar detection and frequency measurement system

A photonics-assisted joint radar detection and microwave frequency measurement system is proposed. A broadband dual-chirp linear frequency-modulated (LFM) signal is generated based on an optically injected semiconductor laser, which is later shared by two functional modules. Thanks to the photonics-assisted broadband signal generation method, both radar detection with high resolution and microwave frequency measurement with high accuracy can be realized without using high-speed electronic devices. In the experiment, a two-dimensional resolution of 1.25 cm × 1.33 cm and a measurement error within ±50 MHz are achieved in radar detection and frequency measurement, respectively.

Terahertz meta-polarizers for simultaneous control of the amplitude, phase, and polarization

Terahertz (THz) waves hold immense potential for advancements in areas such as in 6G wireless communications, non-destructive testing, and biomedicine. However, the underdeveloped control level of THz devices has been a severe obstacle. Here, a THz meta-polarizer, comprising rectangular metallic holes, is proposed to achieve the simultaneous control of the amplitude, phase, and polarization. Each unit of this meta-polarizer can function as a miniature linear polarizer. The component polarized along the short side can pass through the unit, while the component polarized orthogonally is unable to do so. Leveraging the principles of the Malus' law, we can adjust the desired amplitude distribution of the transmitted x-polarized wave by modifying its polarization distribution. This adjustment is made possible by tuning the azimuth angle of each hole. The phase delay of the transmitted wave can be further manipulated by adjusting the hole dimensions perpendicular to the polarization direction. For given near-field amplitude distributions, the required phase distributions for generating far-field target holograms can be calculated using the Gerchberg–Saxton algorithm based on Fresnel diffraction theory. The meta-polarizer then realizes calculated phase distributions, enabling the creation of the required holograms. As a proof of concept, we designed THz meta-polarizers that can achieve two- and four-level amplitude holograms in the near field, respectively. Moreover, we also manipulated the phase distributions, allowing us to decouple the near- and far-field holograms. The experimental results agree well with the simulations. Owing to its ability to simultaneously control the phase, amplitude and polarization, the proposed THz meta-polarizer holds great application potential in 6G wireless communications, radar detection systems, and holography.

The microwave absorption properties variation with temperature of RF/SiO2 and improved microwave absorption by periodic structure

With the widespread application of radar detection systems in the military field, the development of radar stealth technology can effectively reduce the characteristic signals of targets, which was of great significance for the protection of important military targets. In this paper, the resorcinol/formaldehyde (RF) reinforced silica aerogel (RF/SiO2) was synthesized successfully. The RF was introduced into the sol/gel technology to build a porous skeleton, and the SiO2 aerogel was prepared by atmospheric pressure drying. The RF/SiO2 aerogel was then heat treated, and the microstructure and physical properties of the RF/SiO2 aerogel before and after heat treatment were characterized to study the influence of the heat treatment temperatures on the RF/SiO2 aerogel. With the increase of the heat treatment temperature, its electromagnetic wave absorption performance had been significantly improved. When the heat treatment temperature reached 1500 ℃, the RF/SiO2 aerogel possessed a minimum reflection loss of − 36.42 dB at 1.95 mm and an effective bandwidth of 2.94 GHz at 2.00 mm within the radar band of 8.00–12.00 GHz. In addition, this paper used CST software to optimize and design the periodic structure of metamaterial based on the RF/SiO2 aerogel. The optimization design of the patch size and substrate thickness on the substrate surface provided a reference for the future application of this material in specific radar stealth fields.

All-Metal Coding Metasurfaces for Broadband Terahertz RCS Reduction and Infrared Invisibility

With the rapid advancement of modern technology and radar detection systems, electromagnetic (EM) stealth technology has become increasingly significant, particularly in aircraft stealth and military radar applications. In this work, an all-metal metasurface is designed for broadband terahertz radar cross-section (RCS) reduction and infrared invisibility. The all-metal metasurface possesses extremely low infrared emissivity and high polarization conversion in the terahertz band. Through the joint simulation of MATLAB and CST, a genetic algorithm is used to optimize the random phase distribution of 2, 3, and 4-bit metasurfaces, so that the reflected wave is scattered to achieve broadband terahertz RCS reduction. Simulation results show that the metasurface can simultaneously achieve broadband terahertz RCS reduction in 3–5 THz and infrared invisibility in 24–38 THz (8–12.5 μm). The RCS reduction of the coding metasurface is greater than 10 dB compared to the metal plate, and the maximum RCS reduction of the 4-bit metasurface can reach 21.1 dB. Compared to the traditional design method, the proposed method can reduce time consumption and find the optimal result to achieve high performance. We believe the proposed method can provide significant guidance for surface coating in camouflage applications and opens up new possibilities for improving the information capacity of coding metasurfaces.