Radar Detection Research Articles

Characteristic Description and Statistical Model-Based Method for Sea Clutter Modeling

The modeling and analysis of sea clutter are of great significance in radar target detection studies in marine environments. Sea clutter typically exhibits non-Gaussian characteristics and spatiotemporal correlations, posing challenges for modeling, especially when generating simulation data of continuous correlated non-Gaussian random processes. This paper proposes a novel method for sea clutter modeling. First, feature description functions are constructed to individually characterize the amplitude, temporal, and spatial correlations of sea clutter, allowing for an accurate depiction of its characteristics with fewer parameters. Subsequently, simulation data are generated based on these feature description functions, satisfying the amplitude distribution, temporal correlation, and spatial correlation characteristics of sea clutter. Additionally, complex signal forms are introduced in the underlying signal processing to generate texture and speckle components of sea clutter, enhancing the alignment of simulation data with actual data. Through comparison with measured sea clutter data, the proposed method has been shown to accurately simulate complex sea clutter with real-world characteristics.

An interference power allocation method against multi-objective radars based on optimized proximal policy optimization

Aiming at the problem of interference resource scheduling in cognitive electronic warfare, a multi-objective interference power allocation method based on the proximal policy optimization (PPO) framework is proposed in this paper. Firstly, the confrontation between jammers and multi-objective radar networks is mapped as the interaction between the agent and the environment, and the radar target detection model under suppression interference is established. On this basis, an interference power allocation model against multi-objective radars based on PPO framework is constructed. Moreover, a reward normalization mechanism is introduced to optimize the reward setting, and an interference power allocation method based on optimized PPO is proposed. Meanwhile, this paper constructs a confrontation scenario in which the jammer covers the target aircraft to break through the multi-objective radar network. Simulation experiments are conducted based on this scenario to verify the effectiveness of the method proposed in this paper. The interference power allocation method proposed in this paper can intelligently adjust the power allocation scheme of the jammer according to the electromagnetic situation on the battlefield, optimize the resource utilization of the jammer, and occupy the initiative on the battlefield.

Fine-scale behaviour and population estimates of endangered sooty albatross suggest low exposure (while not excluding high sensitivity) to bycatch

Recent developments in assessing species-specific seabird bycatch risks have demonstrated that fine-scale approaches are essential tools to quantify interactions with fishing vessels and to understand attraction and attendance behaviours. Matching boat movement with bird tracking data specifically allows us to investigate seabird-fisheries interactions for cryptic species for which on-board information is critically lacking. The sooty albatross Phoebetria fusca overlaps with fisheries throughout its range and is known to be vulnerable to incidental bycatch. Combining radar detectors, GPS and behavioural data from individuals from the Crozet Islands and boat locations during the incubation period, we investigated interactions of sooty albatrosses with fisheries in the southern Indian Ocean. Individuals foraged mostly in sub-tropical international waters, where they only encountered a small number of boats, all reporting to the Automatic Identification System (AIS). The low interaction rate during this period may suggest that sooty albatrosses are not strongly attracted to fishing vessels, and that attraction rates may vary between populations. However, this result should be interpreted with caution due to the low sample size and fishing effort during the study period, as these observations may conceal a higher bycatch risk during intense fishing effort and/or energetically demanding periods. The conservation status of this species requires further data to be collected throughout the annual cycle to provide an accurate assessment of the threat.

Integrated Anti-Aliasing and Fully Shared Convolution for Small-Ship Detection in Synthetic Aperture Radar (SAR) Images

Synthetic Aperture Radar (SAR) imaging plays a vital role in maritime surveillance, yet the detection of small vessels poses a significant challenge when employing conventional Constant False Alarm Rate (CFAR) techniques, primarily due to the limitations in resolution and the presence of clutter. Deep learning (DL) offers a promising alternative, yet it still struggles with identifying small targets in complex SAR backgrounds because of feature ambiguity and noise. To address these challenges, our team has developed the AFSC network, which combines anti-aliasing techniques with fully shared convolutional layers to improve the detection of small targets in SAR imagery. The network is composed of three key components: the Backbone Feature Extraction Module (BFEM) for initial feature extraction, the Neck Feature Fusion Module (NFFM) for consolidating features, and the Head Detection Module (HDM) for final object detection. The BFEM serves as the principal feature extraction technique, with a primary emphasis on extracting features of small targets, The NFFM integrates an anti-aliasing element and is designed to accentuate the feature details of diminutive objects throughout the fusion procedure, HDM is the detection head module and adopts a new fully shared convolution strategy to make the model more lightweight. Our approach has shown better performance in terms of speed and accuracy for detecting small targets in SAR imagery, surpassing other leading methods on the SSDD dataset. It attained a mean Average Precision (AP) of 69.3% and a specific AP for small targets (APS) of 66.5%. Furthermore, the network’s robustness was confirmed using the HRSID dataset.

A Multi-Parameter Optimization Method for Electromagnetic Characteristics Fitting Based on Deep Learning

Electromagnetic technology is widely applied in numerous fields, and precise electromagnetic characteristic fitting technology has become a crucial part for enhancing system performance and optimizing design. However, it faces challenges such as high computational complexity and the difficulty in balancing the accuracy and generalization ability of the model. For example, the Radar Cross Section (RCS) distribution characteristics of a single corner reflector model or Luneberg lens provide a relatively stable RCS value within a certain airspace range, which to some extent reduces the difficulty of radar target detection and fails to truly evaluate the radar performance. This paper aims to propose an innovative multi-parameter optimization method for electromagnetic characteristic fitting based on deep learning. By selecting common targets such as reflectors and Luneberg lens reflectors as optimization variables, a deep neural network model is constructed and trained with a large amount of electromagnetic data to achieve high-precision fitting of the target electromagnetic characteristics. Meanwhile, an advanced genetic optimization algorithm is introduced to optimize the multiple parameters of the model to meet the error index requirements of radar target detection. In this paper, by combining specific optimization variables such as corner reflectors and Luneberg lenses with the deep learning model and genetic algorithm, the deficiencies of traditional methods in handling electromagnetic characteristic fitting are effectively addressed. The experimental results show that the 60° corner reflector successfully realizes the simulation of multiple peak characteristics of the target, and the Luneberg lens reflector achieves the simulation of a relatively small RCS average value with certain fluctuations in a large space range, which strongly proves that this method has significant advantages in improving the fitting accuracy and optimization efficiency, opening up new avenues for research and application in the electromagnetic field.

Study of Radio Reconnaissance Receivers Designed on the Basis of Solid-state Filters

Radio intelligence systems are vital technological resources that contribute significantly to contemporary security, defense, and intelligence efforts. They enable the monitoring of adversaries' communication signals, the decryption of encrypted information, and the acquisition of strategic advantages. These systems are essential for military operations, counter-terrorism efforts, national security, and strategic planning. As radio intelligence continues to evolve, it is anticipated that more effective and multifunctional systems will emerge in the future.In modern electronic warfare, radio reconnaissance stations are essential for effective signal detection, classification, and geolocation of enemy communication and radar signals. Current advancements in radio reconnaissance technology focus on enhancing measurement accuracy and reliability, crucial for precise threat identification and situational awareness. Key improvements include implementing high-resolution digital signal processing algorithms, advanced filtering techniques, and real-time data fusion to increase detection precision across complex environments. Furthermore, there is a strategic emphasis on reducing the physical dimensions and weight of these stations to improve mobility and operational efficiency in diverse field conditions. Innovations in miniaturized components, lightweight materials, and compact antenna arrays are central to these design objectives, facilitating deployment in both manned and unmanned platforms. This approach not only optimizes the performance of reconnaissance stations but also broadens their applicability across various operational scenarios, making them integral to modern defense systems. In the article, the application of solid-state filters in the spectrum analyzers of radio reconnaissance receivers was considered in order to increase the measurement accuracy and reliability of radio reconnaissance stations, and to reduce their size and weight. In the article, the modeling of load-connected filter structures was conducted to calculate quasi-statistical potential diagrams, which allows for the optimization of their parameters and reduces the design time of devices based on these filters. The research work was carried out in military unit No. N.

Online penetration trajectory planning using blind areas of network radar system for an unmanned combat aerial vehicle

PurposeA densely distributed network radar system compensates for the disadvantages of sparse radars and poses a significant threat to low-altitude penetration by an unmanned combat aerial vehicle (UCAV). Unlike previous studies, this paper aims to consider radar blind areas and proposes a rapid online method for planning low-altitude penetration paths.Design/methodology/approachFirst, the optimization problem coupling digital elevation map (DEM), radar detection probability model and nonholonomic UCAV kinematic model is established. Second, an online solution framework of penetration path planning is constructed. An intervisibility method and map scaling are proposed to generate a detection probability map (DPM). Through completeness and consistency analysis, an adaptive hybrid A* algorithm with fast local replanning strategy is proposed to search a path that takes into account time-consuming, detection probability under nonholonomic constraints. Finally, three scenarios of multiple known, pop-up and vanished static radars are simulated using C++. The computational performance is compared and analyzed.FindingsThe results showed that the proposed online method can generate low-detection-probability penetration paths within subseconds.Originality/valueThis paper provides a new online method to plan UCAV penetration trajectory in military and academic contexts.

Seamless Optimization of Wavelet Parameters for Denoising LFM Radar Signals: An AI-Based Approach

Linear frequency modulation (LFM) signals are pivotal in radar systems, enabling high-resolution measurements and target detection. However, these signals are often degraded by noise, significantly impacting their processing and interpretation. Traditional denoising methods, including wavelet-based techniques, have been extensively used to address this issue, yet they often fall short in terms of optimizing performance due to fixed parameter settings. This paper introduces an innovative approach by combining wavelet denoising with long short-term memory (LSTM) networks specifically tailored for LFM signals in radar systems. By generating a dataset of LFM signals at various signal-to-noise Ratios (SNR) to ensure diversity, we systematically identified the optimal wavelet parameters for each noisy instance. These parameters served as training labels for the proposed LSTM-based architecture, which learned to predict the most effective denoising parameters for a given noisy LFM signal. Our findings reveal a significant enhancement in denoising performance, attributed to the optimized wavelet parameters derived from the LSTM predictions. This advancement not only demonstrates a superior denoising capability but also suggests a substantial improvement in radar signal processing, potentially leading to more accurate and reliable radar detections and measurements. The implications of this paper extend beyond modern radar applications, offering a framework for integrating deep learning techniques with traditional signal processing methods to optimize performance across various noise-dominated domains.

Reconfigurable Intelligent Surfaces Assisted NLOS Radar Anti Jamming Using Deep Reinforcement Learning

The complexity of the radar environment increases with technological advancement, especially when considering the difficulties presented by repeating jammers. These jammers can impede radar detection, especially when they create false targets in non-line-of-sight (NLOS) situations. This study focuses on optimizing the phase shifts of Reconfigurable Intelligent Surfaces (RIS) to address the problem of NLOS between a target and radar for detection in order to address these NLOS issues. Specifically, we investigate RIS phase shift optimization using a Genetic Algorithm (GA) to address the challenges posed by repeating jammers across various dynamic scenarios. Our objective is to increase the radar system’s ability to detect actual targets in non-LOS scenarios when repeater jammers are present in the environment. According to the experimental results, this method offers a practical way to mitigate the effects of repeater jammers by improving radar detection performance in NLOS environments.

Pico-Net: a high-precision synthetic aperture radar small ship detection framework based on feature selection and reuse

Pico-Net: a high-precision synthetic aperture radar small ship detection framework based on feature selection and reuse

Research on the Forward Simulation and Intelligent Detection of Defects in Highways Using Ground-Penetrating Radar

The increasing variety and frequency of subgrade defects in operational highways have led to a rise in road safety incidents. This study employed ground-penetrating radar (GPR) detection and forward simulation to analyze the characteristic patterns of common subgrade defects, such as looseness, voids, and cavities. Through the integration of instantaneous feature information from different defect patterns with complex signal techniques, the boundary judgment of structural layers and anomalies in GPR images of various subgrade defects was improved. An intelligent recognition platform was established, and a radar image dataset was created and trained to evaluate the recognition performance of the You Only Look Once (YOLO) v3 and Single-Shot Multi-Box Detector (SSD) algorithms. Evaluation metrics such as precision, recall, F1-score, average precision (AP), and mean average precision (mAP) were used to assess the detection efficiency and accuracy for subgrade defect images. The results showed that YOLO v3 achieved an average detection accuracy of 76.69%, while the SSD achieved 75.07%. This study demonstrates that the reliability of the intelligent recognition and classification of highway subgrade defects can be enhanced by using GPR for non-destructive testing.

Wide band high gain modified-folded-wave-guide slow wave structure for millimeter-wave traveling wave tube amplifier in 6G and beyond back-haul communications

Abstract Millimeter-wave technology is crucial in telecommunications as it enables higher bandwidths and faster data transfer. V-band represents frequencies ranging from 50 to 75 GHz and is used for short-range back-haul communication, defense radar, inter-satellite communications, and space debris detection. Folded waveguide slow-wave structures (FWG-SWS) are an excellent choice for V-band amplifiers due to their wide bandwidths, large gain, and ease of fabrication using modern lithographic processes. This article presents a modified folded waveguide slow-wave structure (MFW-SWS) for V-band traveling wave tube amplifiers (TWTAs) desirable for multi-gigabit 6G and beyond back-haul communications. The MFW slow-wave structure is an FWG-SWS variant that supports an increased RF phase velocity, accommodating additional SWS length for much-improved amplification. The dispersion analysis effectively provides a 15% operational bandwidth ranging from 64–74 GHz with a reduced phase velocity of three-tenths the speed of light apt for enhanced beam-wave interactions. The two-section SWS has independent attenuators designed to provide over 20 dB attenuation throughout the functional bandwidth effortlessly. Additional wave-guide transitions are introduced to achieve the best attenuation characteristics. Along the axis of the SWS, a square beam tunnel is employed to support a 26 kV round electron beam with a beam current of 70 mA. To contain the electron beam within the beam tunnel, this study used a focusing mechanism with a constant magnetic field of 0.25T, which is highly appropriate for miniaturization and large-scale implementation in 6G back-haul towers. Particle-in-cell (PIC) simulations were performed using the CST (Computer Simulation Technology) Studio Suite, and a gain of over 26 dB with an output power of 50 W was successfully attained across the operational bandwidth.

Study on the accurate detection method of full‐polarimetric ground‐penetrating radar faults in mines based on modified Yamaguchi decomposition

AbstractObtaining information on tectonic tendencies is a prerequisite for intelligent and accurate mining in mines. In the special mine environment, the co‐polarized ground‐penetrating radar can only identify the spatial location of faults, and it is difficult to analyse the inclination information of fault structures. This paper proposes a mine full‐polarimetric ground‐penetrating radar fault tendency detection method based on this. First, based on the stacking characteristics of the coal depositional, this paper analyses the propagation law of the pulse electromagnetic wave in the coal seam and puts forward the assumption of the overlapping echo reflection of the fault structure. The reasonableness of the fault reflection assumption is verified through a numerical simulation study. Second, based on the cutting relationship of the fault to the coal seam, we divided the reflection structure of the fault structure into plane scattering and dihedral angle scattering. We realized the mingled echoes’ decomposition using the improved Yamaguchi decomposition technique. To analyse the applicability of the modified Yamaguchi and Freeman decomposition methods in the identification of fault inclination, we use the upright fault simulation data for the discussion, and we find that the improved Yamaguchi decomposition method is more advantageous in the identification of fault inclination in the mine. The decomposition results based on the simulation data of fault models with different dip angles found that when the dip angle of the fault is less than 90°, the scattering of the fault structure is dominated by planar scattering and dihedral angle scattering; when the dip angle of the fault is greater than or equal to 90°, the scattering of the fault structure is dominated by planar scattering, and the scattering power of the dihedral angle model is zero. By analysing the effect of fault strike on the decomposition results, it is found that the fault strike angle has little effect on the identification of fault tendency. Finally, the application potential of this paper's method is tested by constructing complex numerical models and probing experiments. Therefore, the method proposed in this paper can solve the fault tendency identification under a thick coal seam.

Flexible CoNiZn@Ti3CNTx MXene@carbon fabrics with hierarchical structure for efficient electromagnetic wave absorption

The widespread application of 5G technology has significantly exacerbated concerns regarding electromagnetic radiation. However, the rigidity and lack of flexibility of traditional electromagnetic wave (EMW) absorption materials make them ill-suited for protecting humans and certain specialized, complex shapes of equipment. Therefore, there is an increasing urgency to develop flexible EMW absorbing fabrics to effectively mitigate electromagnetic pollution. In this study, hierarchical structure CoNiZn@Ti3CNTx MXene@carbon fabrics (CoNiZn@Ti3CNTx@CF) were constructed from nano-flower structure CoNiZn-MOF, Ti3CNTx MXene and fabrics by self-assembly, in-situ growth and pyrolysis. The carbon fabric acts as a skeleton to form a 3D interconnected conductive network, in which loaded nano-flower structure CoNiZn@Ti3CNTx MXene (CoNiZn@Ti3CNTx@C) composites exists excellent dielectric loss and magnetic loss, so that EMW enters the fabric and optimizes the impedance matching, increasing the multiple reflection and scattering of EMW, thereby effectively attenuating EMW. The minimum reflection loss (RLmin) of CoNiZn@Ti3CNTx@CF reaches -44.51 dB at 14.88 GHz with filling ratio of 15 % and thickness of 1.50 mm and the effective absorption bandwidth (EAB) of CoNiZn@Ti3CNTx@CF reaches 4.32 GHz (13.04–17.36 GHz) at 1.55 mm. The EAB of CoNiZn@Ti3CNTx@CF is expanded to 14.56 GHz (3.44–18.00 GHz) through thickness adjustment (1–5.50 mm), covering most of the S, C, X, and Ku bands. The radar cross-section (RCS) value of CoNiZn@Ti3CNTx@CF is 17.3 dB m2 lower than the perfect electrical conductor (PEC), which effectively reduces the probability of the target being detected by the radar detector. This work provides ideas for design and development of flexible EMW absorbing materials.

Application of Ground Penetrating Radar in Rapid and Non destructive Detection of Urban Tunnels

Abstract Due to the rapid advancement of modern transportation, tunnels have emerged as an important component of transportation infrastructure. A large number of pipelines and tunnels makes the urban underground environment extremely complex. Detecting underground tunnels and pipelines can provide guidance for urban construction. Ground penetrating radar (GPR) possesses attributes such as swift detection speed, no loss, convenient and flexible operation, thereby playing a crucial role in numerous domains. The aim of this research is to verify the feasibility of detecting tunnels with depths on the order of 10 meters, by evaluating tunnel dimensions and depth parameters through a combined approach of simulation and experimental measurements. In this investigation, gprMax was utilized to model and simulate the underground tunnel to establish the correlation between echo data and target position. Additionally, this research employed an 80MHz GPR system to detecting a undergound tunnel and acquiring data which facilitated successful determination of the depth of the underground tunnel based on empirical evidence

Application of ground penetrating radar in potential hazards detection of levee

Abstract Ground-penetrating radar, utilizing electromagnetic wave technology, stands as a crucial geophysical exploration method for analyzing hydraulic engineering defects. In the context of identifying potential hazards in a levee within Yunfu, Guangdong province, GPR technology is deployed. This study delves into the radar’s ability to discern characteristics such as inadequate soil compactness, voids, as well as interference from metal pipes and wires. By scrutinizing these radar signatures, the primary causes underlying the development of cracks and uneven settlement across multiple sections of the levee are identified. The insights garnered from this analysis serve as valuable reference points for leveraging ground-penetrating radar technology in the detection of potential hazards within hydraulic engineering contexts.

Moving Target Detection in Clutter Environment Based on Track Posture Hypothesis Testing

ABSTRACTMoving target detection in a heavy clutter area is a challenging problem in the field of radar automatic target detection. Within the framework of track posture hypothesis testing, the radar‐blips‐oriented target detection algorithms named ErgSad and RanSaD are proposed for single target and formation targets detection, respectively in this paper. With the assumption that target motion is a linear Gaussian process in a short time, the algorithms use dual blips in different scans to generate the track hypothesis and test the hypothesis with residual blips in other scans to determine the existence of moving targets. To reduce time consumption and minimize the error of model estimation, the sampling rules and the supporting domain related to timestamps of track hypothesis models were designed. Simulation data experiments show that the proposed algorithms have more superior performance to detect targets than the state‐of‐the‐art algorithms in the serious clutter environment.

Research on position Model of Surface-to-air Missile Defense against Cruise Missile Based on Rule GA

Abstract The deployment of weapon systems prior to the engagement of anti-cruise missiles constitutes the primary research challenge in surface-to-air missile operations. Initially, it is essential to analyze operational requirements and scientifically delineate the guided radar detection area, kill zone, and interception zone based on physical realities. A model should be established to ascertain the fundamental conditions for site deployment, taking into account factors such as position height, shielding angle, and geographic coordinates. In relation to low-altitude approaches by adversarial forces, key considerations include the depth and breadth of firepower from the weapon system in the attack direction, firepower density in that direction, and lethality probability. An Operational Effectiveness model should be developed based on coverage area of firepower, effective interceptions per unit time, and lethality probabilities of weapon systems to evaluate each location’s effectiveness. Finally, leveraging land selection principles and external information coordination mechanisms will facilitate establishing a genetic algorithm-based strategy for deploying surface-to-air missile systems. The validity of this algorithm has been demonstrated through practical examples.

Research on Interception Model of Surface-to-air Missile Anti-cruise Missile Weapon System

Abstract In air defense operations, resisting cruise missile attacks is a major challenge. Firstly, based on judging the direction and route of the cruise missile weapon system, the paper establishes the radar detection zone algorithm, the killing zone of surface-to-air missile weapon system and the interception model of surface-to-air missile weapon system needed in the process of physical interception. Secondly, according to whether the effective interception condition is satisfied, an algorithm is established to judge the surface-to-air missile weapon system on several fixed positions. Lastly, it gives the maximum interception expectation of the incoming route to achieve effective interception. We set up scenarios through examples to reasonably divide the Red Army’s combat area, analyze quantitatively the effectiveness of blue ground-to-air missiles intercepting red cruise missiles under different terrain conditions, after comprehensively judging the entry situation of detection and killing zones and calculating whether there is an interception arc, the interception efficiency can be calculated. This model incorporates terrain constraints, making the results more reasonable than other models. It provides a scientific basis for command assisted decision-making.

Modeling and calculation of ground penetrating radar detection on rough interface of soil media

Abstract Ground penetrating radar technology is an effective underground physical detection method. In order to better study the ground penetrating radar echo characteristics of complex soil media rough interfaces, a soil rough surface electromagnetic scattering model based on fractal rough surface geometry modeling and soil media dielectric properties modeling is established. The mixed soil characteristics including sand content, water content, clay content, and sand particle density are analyzed, and the radar echo image characteristic of soil rough media interfaces under different fractal dimensions is discussed.