Radar Detection Research Articles

Accurate and efficient layered-medium modeling of wave propagation in inshore seawater for ground penetrating radar detection

The electromagnetic (EM) wave-based ground penetrating radar (GPR) finds extensive applications due to its ability to propagate through various media with significant penetration power. In the inshore shallow sea regions, the water has a low salinity and high sediment content with a nonuniform distribution. Leveraging GPR in these regions presents a novel approach to acquiring seafloor topographic knowledge. Although the oceanic environment exhibits continuous stratification, we propose a layered-medium model method (LMMM) for simplification. Through meticulous analysis of EM wave propagation within this model, GPR enables the identification of interfaces between water-suspended sediment transition zones and marine sediments, along with effective discrimination of sediment types. The wave propagation at the frequency of f = 9.0 MHz in the GPR is simulated in the environment of the shallow coast and the distribution of the magnetic field is obtained in different directions. The simulation results are compared with those of the well-known HFSS, thus validating the effectiveness of the proposed approach.

Lightweight deep neural network for radio frequency interference detection and segmentation in synthetic aperture radar

Radio frequency interference (RFI) poses challenges in the analysis of synthetic aperture radar (SAR) images. Existing RFI suppression systems rely on prior knowledge of the presence of RFI. This paper proposes a lightweight neural network-based algorithm for detecting and segmenting RFI (LDNet) in the time-frequency domain. The network accurately delineates RFI pixel regions in time-frequency spectrograms. To mitigate the impact on the operational speed of the entire RFI suppression system, lightweight modules and pruning operations are introduced. Compared to threshold-based RFI detection algorithms, deep learning-based segmentation networks, and AC-UNet specifically designed for RFI detection, LDNet achieves improvements in mean intersection over union (MIoU) by 24.56%, 13.29%, and 7.54%, respectively.Furthermore, LDNet reduces model size by 99.03% and inference latency by 24.53% compared to AC-UNet.

A Review on Radar-Based Human Detection Techniques.

Radar systems are diverse and used in industries such as air traffic control, weather monitoring, and military and maritime applications. Within the scope of this study, we focus on using radar for human detection and recognition. This study evaluated the general state of micro-Doppler radar-based human recognition technology, the related literature, and state-of-the-art methods. This study aims to provide guidelines for new research in this area. This comprehensive study provides researchers with a thorough review of the existing literature. It gives a taxonomy of the literature and classifies the existing literature by the radar types used, the focus of the research, targeted use cases, and the security concerns raised by the authors. This paper serves as a repository for numerous studies that have been listed, critically evaluated, and systematically classified.

The first results of the hydrogen cyanide (HCN) interferometer measuring experimental research apparatus for electromagnetic science (ERAES) for hypersonic vehicle plasma in near space.

The formation of a plasma sheath on the surface of spacecraft or satellites during high-speed atmospheric entry is a significant factor that affects communication and radar detection. Experimental research apparatus for electromagnetic science can simulate this plasma sheath and study the interaction mechanisms between electromagnetic waves and plasma sheaths. Electron density is a crucial parameter for this research. Therefore, in this paper, a HCN heterodyne interferometer has been designed to measure the electron densities of the device, which range from 1 × 109 to 3 × 1013cm-3 and the pressure ranges from 50 to 1500Pa. The light source is a HCN laser with a wavelength of 337 µm, which exhibits higher spatial resolution compared to microwave interferometers. The interferometer is configured as a Mach-Zehnder interferometer, which generates an intermediate frequency through the Doppler shift achieved by a rotating grating. The spatial and temporal resolution of the HCN interferometry reach ∼14mm and 100 µs, respectively. Antenna-coupled ALGaN/GaN-HEMT detectors have been utilized, as they possess higher sensitivity-with a typical reduction factor responsivity of around 900 V/W-than VDI planar-diode Integrated Conical Horn Fundamental Mixers in HCN interferometry. Recently, the initial results of the HCN interferometer designed for ERAES have been obtained during an experimental campaign, demonstrating a phase resolution of up to 0.04π.

Autonomous radar interference detection and mitigation using neural network and signal decomposition

<p>Autonomous radar interference is a challenging problem in autonomous vehicle systems. Interference signals can decrease the signal-to-interference-noise ratio (SINR), and this condition decreases the performance detection of autonomous radar. This paper exploits a neural network and signal decomposition to detect and mitigate radar interference in autonomous vehicle applications. A neural network (NN) with four inputs, one hidden layer, and one output is trained with various signal-to-noise (SNR), interference radar bandwidth, and sweep time of autonomous radar. Four inputs of NN represent SNR, mean, total harmonic distortion (THD), and root means square (RMS) of the received radar signal. Variational mode decomposition (VMD) and zeroing based on a constant false alarm rate (CFAR-Z) are used to mitigate radar interference. VMD algorithm is applied to decompose interference signals into multi-frequency sub-band. As a result, the proposed neural network can detect radar interference, and NN-VMD-CFAR-Z can increase SINR up to 2dB higher than the NN-CFAR-Z algorithm.</p>

Joint waveform design of radar detection, wireless communication and jamming based on chaotic composite modulation

AbstractIn this letter, we address the problem of designing the joint waveform of radar detection, wireless communication and jamming. In the current most powerful filter‐bank multi‐carrier comb spectrum framework, a parameter‐agile time–frequency structure between symbols is elaborated to improve the uncertainty of waveform generation architecture. Furthermore, a novel composite modulation strategy that combines intra‐symbol chaos‐based radar frequency modulation and inter‐symbol chaos‐based phase modulation is proposed for a substantial jamming performance improvement. Simulation results verify that the proposed joint waveform produces a superior jamming performance while satisfying the requirements of radar detection and wireless communication.

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).

Multifunctional electromagnetic wave absorbing carbon fiber/Ti3C2TX MXene fabric with ultra-wide absorption band

In order to provide electronics and aerospace equipment with effective immunity to electromagnetic interference and to improve their adaptability to very harsh electromagnetic environments, high-performance electromagnetic wave-absorbing materials are urgently needed for civil and military applications. Unfortunately, their effective absorption bandwidth width is still unsatisfactory (<10 GHz) due to the insufficient regulation of their component mixing and microstructure. By employing nanocage-structured UiO-66 as a doping phase, MXene/ZrO2/tungsten-doped carbon (MXene/ZrO2/W-C) interpenetrating fiber networks were prepared by the electrostatic spinning method. According to the experimental results, the introduction of the dopant phase and the formation of the interpenetrating network significantly improves impedance matching, resulting in a wide absorption bandwidth of 11.12 GHz for the composites, completing the coverage of the X and Ku bands. The radar scattering cross section of MXene/ZrO2/W-C has been simulated using three-dimensional electromagnetic field simulation, and as a result of its excellent stealth performance, it is able to generate corresponding electromagnetic responses in radar detection environments that exhibit different far-field emission bands. Further, MXene/ZrO2/W-C exhibits outstanding hydrophobicity, corrosion resistance, and temperature resistance. These characteristics allow it to be used for the preparation of electromagnetic wave (EMW) absorbing devices for complex environmental applications. It is important to note that the results of this study will be extremely useful in the development of composites based on MXene with ultra-wide absorption bands as well as in the analysis of EMW absorption mechanisms.

Active reconfigurable intelligent surface‐aided multiple‐input‐multiple‐output radar detection in the presence of clutter

AbstractReconfigurable intelligent surface (RIS) refers to a two‐dimensional smart surface consisting of massive number of reflecting elements. Both active RIS (ARIS) and passive RIS (PRIS) are promising technologies that can adapt and reconfigure the wireless environment, enhancing the reliability and capacity of wireless networks. The authors focus on enhancing the detection ability of ARIS/PRIS‐aided multiple‐input‐multiple‐output radar system in the presence of signal‐dependent clutter. Constant‐envelope constraint is imposed on the sought radar waveforms to improve the practicability of the radar system. To tackle the resultant non‐convex problem, a cyclic method based on minorisation–maximisation and alternating direction method of multipliers is derived to iteratively optimise the receive filters, the transmit waveforms, and the RIS coefficients. The major finding is that the distance between the radar and the ARIS has little to no impact on the radar signal‐to‐interference‐plus‐noise ratio (SINR). For the PRIS‐aided radar system, a simplified model can significantly improve the operational efficiency and has little impact on the radar SINR. Numerous results verify the effectiveness of the proposed algorithm.

Forward Simulation and Complex Signal Analysis of Concrete Crack Depth Detection Using Tracer Electromagnetic Method

Cracks are the most typical faults of concrete structures, and their extension can lead to structural fracture. However, when cracks develop inside a structure, the most important depth information is invisible and difficult to measure. The tracer electromagnetic method is an effective technique for detecting the depth of concrete cracks, but since concrete is a multiphase stochastic composite material, its complex internal structure often interferes with the radar detection results, making the conventional radar interpretation technique difficult. In this study, the detection results for concrete crack depth detection based on the tracer electromagnetic method were comprehensively analyzed by combining the complex signal analysis technique, using transient information such as amplitude, phase, and frequency in order to improve the precision and accuracy of radar signal interpretation. In this study, a numerical model was established to determine whether typical cracks such as vertical cracks and diagonal cracks contain indicators or not, and the ground-penetrating radar forward simulation software was used to perform forward simulation of the numerical model and analyze the forward results. The complex signal analysis technique was used to obtain the response characteristics of typical cracks when they did and did not contain the indicator, and the complex signal was finally analyzed by combining it with the actual crack depth detection data. The results show that the tracer electromagnetic method can significantly improve the crack bottom’s reflection ability for radar signals, and when the crack bottom contains an indicator, the amplitude of the reflected signal at the bottom of the crack is enhanced, the phase is reversed, and the frequency is reduced. The distribution of the crack morphology and the location of the crack bottom can be analyzed more conveniently by using the complex signal analysis technique.

An ultralight, eco-friendly 3D porous carbon aerogel derived from cotton nano-cellulose for infrared stealth and microwave absorption

As multi-band military detection technology advances in the field of information warfare, stealth materials that are only applied for single frequency bands have extremely poor effectiveness in avoiding detection interference. Thus, it is urgent to develop stealth materials with compatible performance in multiple bands, especially infrared-radar compatible stealth materials. However, integrating infrared-radar compatiblity into one particular material remains a significant challenge. Inspired by the methods of structure design and component selection, this research provided a cotton nano-cellulose-based carbon aerogel with three-dimensional (3D) macro-mesoporous interface space for high-performance infrared-radar compatibility. When the density is only 0.005 ± 0.002 g cm−3, the infrared emissivity exhibits as low as 0.56 in 3–5 μm band and 0.52 in 8–14 μm band, the minimum reflection loss (RLmin) attains −19.8 dB, and the maximum effective microwave absorption bandwidth (EAB) covers 4.64 GHz. Additionally, computer simulation technology (CST) can be used to simulate the optimum radar cross section (RCS) reduction value of 15.86 dB m2, indicating that the aerogel has great potential in reducing the probability of being detected by radar detectors. Therefore, the lightweight porous carbon aerogels not only employ renewable and eco-friendly biomass materials, but also show great significance for high-performance infrared-radar compatibility, which is in favor of protecting human security all over the world.

MTD precise jamming technology based on intra-pulse and inter-pulse frequency-phase joint modulation

For moving target detection (MTD) radar, existing jamming systems face problems such as poor controllability and strong regularity of two-dimensional false target characteristics. Time-frequency-phase modulation technology has great potential for active jamming and has received widespread attention. In this article, we propose a precise jamming method based on intra-pulse and inter-pulse frequency-phase joint modulation to control the two-dimensional distribution of false targets. First, construct periodic phase and segmented frequency modulation functions. Secondly, the intra-pulse phase modulation period Ts1 and frequency modulation amount ξl1 accurately control the positions of multiple false targets; the inter-pulse phase modulation period Ts2 and frequency modulation amount ξl2 accurately control the velocity of multiple false targets. Finally, the amplitude of the two-dimensional false target is accurately controlled by adjusting the phase modulation coefficient φi, phase modulation and frequency modulation duty cycles ηs, ηl. Compared with other jamming algorithms, the jamming distribution is more flexible and controllable, which verifies the superiority of this method.

Fusion-based modeling of an intelligent algorithm for enhanced object detection using a Deep Learning Approach on radar and camera data

Object detection, the process of detecting and classifying objects within a given environment, forms the foundational element. Multisensory fusion incorporates data from diverse sensors, like radar and cameras, to refine the reliability and accuracy of detection. Further, Radar and camera data fusion refine this process by integrating the unique strength of both technologies, which leverage the radar's proficiency in adverse weather conditions and the camera's high-resolution imaging. This incorporation enhances the object detection systems, which enables them to effectively operate across the spectrum of scenarios, from autonomous vehicles navigating challenging weather to surveillance systems monitoring critical infrastructure. Deep learning (DL), a branch of machine learning (ML), empowers this system with the capability to learn complex representations and patterns directly from the data, which enables them to generalize and adapt to new situations. By integrating the advanced methodology, we can develop strong perception system capable of interpreting and detecting objects accurately in dynamic and diverse environments, from autonomous vehicles navigating urban landscapes to surveillance systems monitoring complex environments. This study designs an Intelligent Algorithm for Enhanced Object Detection Using Deep Learning Approach on the Radar and Camera Data Fusion (IAEOD-DLRCDF) technique. The presented IAEOD-DLRCDF technique uses multi-angle joint calibration where the spatial sparse alignment of the heterogeneous data of the camera and Radar is realized with image falsification disregarded. Besides, the IAEOD-DLRCDF technique applies YOLOv8 object detector for radar and camera target detection individually which are then integrated with the image plane. Moreover, the detected objects are then classified via the bidirectional long short-term memory (BiLSTM) model. Furthermore, the Adam optimizer is used for the optimum hyperparameter selection of the BiLSTM network which results in a better recognition rate. The performance assessment of the IAEOD-DLRCDF method is tested under benchmark dataset. The empirical analysis stated that the IAEOD-DLRCDF method gains better performance over other models.

Interference mitigation and target detection for automotive FMCW radar with range-Doppler sparse regularization

Interference mitigation and target detection for automotive FMCW radar with range-Doppler sparse regularization

Compatible metasurface for ultra-wideband radar and switchable infrared stealth

In response to the rapid advancements in radar detection technology and the widespread deployment of infrared sensors, single-function stealth materials are increasingly challenged to meet the sophisticated demands of concealment within complex electromagnetic environments. As a result, there is a pressing need for research into metamaterial structures that can simultaneously deliver ultra-wideband radar stealth and controllable infrared invisibility. Here, a novel metamaterial structure was proposed and realized, comprising vertically integrated infrared stealth and radar stealth layers, with the aim of accomplishing both ultra-wideband radar stealth and controlled infrared invisibility. Coded units were designed based on the geometric phase modulation mechanism and then arrayed through a random matrix strategy optimized by a genetic algorithm, yielding a radar stealth layer characterized by outstanding properties such as ultra-wideband radar stealth and insensitivity to polarization states. A temperature-adaptive infrared stealth switching function was successfully achieved by incorporating vanadium dioxide, a phase-change material, into the infrared stealth layer, exploiting its insulator-to-metal phase transition at a critical temperature. The fabrication and performance testing of the samples have further validated the practicality and rationality of the design scheme. This work can not only open up innovative pathways for the advancement of multi-band compatible stealth technology but is also of great significance for the application of electromagnetic shielding and stealth technologies in complex settings.

Radar detectability of light aircraft micro‐Doppler modulation

AbstractThe critical role of specifying micro‐Doppler mode performance in the modelling and development of modern radar systems is investigated. The authors focus on the detection of micro‐Doppler modulation from light aircraft, analysing data from eight helicopters and nine propeller aircraft. With the growing need for accurate target classification in radar technology, incorporating micro‐Doppler detection metrics into radar performance specifications has become increasingly important. This research offers a novel approach to measuring the detectability of micro‐Doppler modulation relative to returns from the main fuselage. The investigation covers the impacts of various preprocessing techniques, polarisation, and aspect angle on detection capabilities. Findings reveal that, on average, micro‐Doppler modulation from propellers is detectable at distances between 50% and 100% of the range at which the fuselage is detected. For helicopters, this range decreases to between 30% and 80%. Additionally, the study introduces empirically derived statistical models designed to predict micro‐Doppler detection ranges in relation to fuselage returns, enhancing the predictability and specificity of radar system performance. This novel contribution presents a basis for improving radar system specifications, leading ultimately to more predictable and reliable light aircraft classification.

Investigations into optimum non‐periodic bursts scheme in radar pulse train designed for fixed‐wing drone detections by multi‐function radar

AbstractDrone surveillance with multi‐function radar (MFR) can benefit a lot from the careful radar pulse train organisation into dedicated sub‐RPTs serving particular needs of tasks. In contemporary scenarios of MFR applications, where navigation, search and rescue, and natural environment safeguarding are to be shared, of particular importance is a case where airborne small drone detection radar performs other tasks concurrently. Resource sharing within the radar unit is based on variables and unpredictable circumstances, leading to strains in resources in MFR. Consequently, such constraints limit MFR functioning with optimal drone sensitivity for its own sake. To ensure a high quality of radar services performed quickly, with a low DC power consumption, the task sharing approach by the common MFR platform, non‐periodic bursts emission of radar pulse energy has gained our major attention. At the same time, it was assumed that bursts feature periodic time intervals between radar pulses, which is a favourable circumstance in the control and implementation of signal processing. Each sub‐RPTs is designed to cope with particular detection tasks. One to four linear polarisations (VV, VH, HV, HH) are activated. The focus of the investigation was on the radar emitting a few thousand pulses per second, but only a few 10 pulses per second were expected to be required to detect small airborne drones. The core of the research study was the optimisation process of the scheme operating with 4 to 16 pulse bursts optimised for drone detection with a modest use of resources at MFR. While performing several activities, the X‐band MFR platform called ENAVI was developed in‐house. The proposed non‐periodic burst scheme optimisation approach was validated with in‐field tests with the ENAVI radar model. The in‐air target was a 3‐metre fixed‐wing drone having low RCS and flying at different altitudes with a speed close to 200 km/h. The radar antenna was a 26 dBi low‐profile dual‐polarisation array making it feasible to detect drones remaining a few degrees off the antenna breadboard direction. A very high likelihood of detection of drones from a few kilometres distance was demonstrated within one second. The authors present the effectiveness of the cell‐averaging technique which proved to correctly detect drones within a 7‐km range, and how MFR radar echoes can be used for basic weather surveillance. One vertical VV polarisation was used for drone detection and two VV and VH polarisations for weather observation. Weather radar capabilities were examined against heavy rain clouds imposing a hazard to the safety of drone flights.

The influence of interlayer bonding conditions on the propagation laws of reflective cracks in semi-rigid base pavement based on the DEM and GPR

Reflection cracks represent the predominant internal distress in semi-rigid base asphalt pavements and often accompanied by additional secondary issues that detrimentally affect the pavement structural bearing capacity. The propagation and deterioration of reflective cracks are affected by many factors, for example the interlayer bonding conditions. It is crucial to identify the propagation and deterioration of reflective cracks to prolong the life of pavement structure. The purpose of this article is to explore the influence of interlayer bonding conditions on the propagation laws of reflective cracks. This work employs the discrete element method(DEM) to explore the propagation patterns of reflection cracks accompanied different bonding conditions between the surface layer and base layer subjected to moving load. The results of discrete element simulation reveal a three-phase progression laws when the reflective cracks expand from the base layer to the pavement surface，involving rapid propagation, stable propagation, and failure process. With the interlayer bond strength decreases, expansion of reflective crack is significantly accelerated. Meanwhile, once the critical bond strength threshold is reached, complete debonding will occur between the surface and base layers. The results are ultimately verified through the core sampling from practical engineering using three-dimensional (3D) ground penetrating radar(GPR) detection, along with the radar image recognition of reflective cracks and poor interlayer bonding. The results will serve as a foundation for researching the mechanism and treatment measures for two internal pavement distress, namely reflection cracks and poor interlayer bonding.

Interrupted-Sampling Repeater Jamming Countermeasure Based on Intrapulse Frequency–Coded Joint Frequency Modulation Slope Agile Waveform

Interrupted-sampling repeater jamming (ISRJ) is widely used in the field of electronic countermeasures, and can severely affect radar detection. Therefore, the problem of ISRJ suppression is a compelling task. In this paper, we propose an ISRJ suppression method based on an intrapulse frequency-coded joint frequency modulation (FM) slope agile waveform. The intrapulse frequency-coded joint FM slope agile waveform is first designed. The delay inserted between subpulses makes the waveform easy to implement in engineering, and the ambiguity function diagram of the waveform approximates the ideal thumbtack type. Next, the echo slices are classified in the fractional domain utilizing the discontinuity of ISRJ and the focusing property of fractional Fourier transform for chirp signals. Then, the target and interference in the interfered echo slices are reconstructed by compressed sensing, and a time-domain filter is constructed based on interference-free echo slices. Finally, the echo signal after interference suppression is further filtered in the time domain to degrade range sidelobes. Simulation results show that the proposed method can effectively suppress three typical types of ISRJ. Moreover, the probability of target detection after interference suppression exceeds 90% when the jamming-to-signal ratio equals 50 dB.

Investigating Intra-Pulse Doppler Frequency Coupled in the Radar Echo Signal of a Plasma Sheath-Enveloped Target

In detecting hypersonic vehicles, the radar echo signal is coupled with an intra-pulse Doppler frequency (I-D frequency) component caused by relative motion of a plasma sheath (PSh) and the vehicle, which can induce the phenomenon of a ghost target in a one-dimensional range profile. In order to investigate the I-D frequency generated by the relative motion of a PSh, this study transforms a linear frequency modulated (LFM) signal into a single carrier frequency signal based on echo signal equivalent time delay-dechirp processing and realizes high resolution and fast extraction of the I-D frequency coupled with the frequency-domain echo signal. Furthermore, by relying on the computation of the surface flow field of the RAMC-II Blunt Cone Reentry Vehicle, the coupled I-D frequency in the radar echo signal of a PSh-enveloped target under circumstances of typical altitudes and carrier frequencies is extracted and further investigated, revealing the variation law of I-D frequency. The key findings of this study provide a novel approach for suppressing anomalies in radar detection of PSh-enveloped targets as well as effective detecting and as robust target tracking.