Radar Field Research Articles

A New Bistatic Huynen Target Parameters Extraction Algorithm: Experimental Validation and Data Analysis

The utilization of polarization information is a hot topic in the bistatic radar field. Bistatic full-polarization scattering dataset and polarization decomposition theories are urgently needed. We have established an indoor bistatic full-polarization scattering measurement system to investigate the target polarization scattering characteristics (PSCs), and a dataset is obtained. Due to its excellent performance, bistatic Huynen target parameters (BHTPs) are selected as the PSCs analysis tool. Then, a new BHTP extraction algorithm is presented, which directly extracts BHTPs from the measured Sinclair matrix. It is a state-of-the-art algorithm for BHTP extraction because of its easy implementation, robustness, and clear physical meanings. Experiment results demonstrate that the proposed algorithm can extract BHTPs accurately.

A Few-Shot Learning Method for SAR Images Based on Weighted Distance and Feature Fusion

Convolutional Neural Network (CNN) has been widely applied in the field of synthetic aperture radar (SAR) image recognition. Nevertheless, CNN-based recognition methods usually encounter the problem of poor feature representation ability due to insufficient labeled SAR images. In addition, the large inner-class variety and high cross-class similarity of SAR images pose a challenge for classification. To alleviate the problems mentioned above, we propose a novel few-shot learning (FSL) method for SAR image recognition, which is composed of the multi-feature fusion network (MFFN) and the weighted distance classifier (WDC). The MFFN is utilized to extract input images’ features, and the WDC outputs the classification results based on these features. The MFFN is constructed by adding a multi-scale feature fusion module (MsFFM) and a hand-crafted feature insertion module (HcFIM) to a standard CNN. The feature extraction and representation capability can be enhanced by inserting the traditional hand-crafted features as auxiliary features. With the aid of information from different scales of features, targets of the same class can be more easily aggregated. The weight generation module in WDC is designed to generate category-specific weights for query images. The WDC distributes these weights along the corresponding Euclidean distance to tackle the high cross-class similarity problem. In addition, weight generation loss is proposed to improve recognition performance by guiding the weight generation module. Experimental results on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset and the Vehicle and Aircraft (VA) dataset demonstrate that our proposed method surpasses several typical FSL methods.

Design of Multi-Parameter Compound Modulated RF Stealth Anti-Sorting Signals Based on Hyperchaotic Interleaving Feedback.

Radio frequency (RF) stealth anti-sorting technology is a research hotspot in the radar field. In this study, the signal design principles of anti-cluster and anti-SDIF sorting were investigated for processes of clustering pre-sorting and sequence-difference-histogram main sorting. Then, in accordance with the signal design principle, a 2D interleaving feedback hyperchaotic system based on the cosine-exponential was designed. A method to modulate the pulse repetition interval (PRI) of the signal parameters and carrier frequency with wide intervals through the hyperchaotic system was developed. Finally, we verified the correctness of the signal design principle, the performance of the hyperchaotic system, and the anti-sorting performance of the designed signal using simulations. The results showed that the signal design principle could guide the signal design. The hyperchaotic system outperformed the classical 1D and 2D chaotic systems and the classical 3D Lorenz systems in terms of randomness and complexity. Anti-cluster sorting and anti-SDIF sorting could be realized by anti-sorting signals modulated by a hyperchaotic system, with the anti-SDIF sorting performance being better than that of the PRI random jitter signal.

Editorial Introduction to JSTQE Special Issue on Lidars and Photonic Radars

The purpose of this special issue is to highlight the recent progress, challenges and trends in the different aspects related to lidars and photonic radars. The emerging field of Lidars and Photonic Radars has opened up new horizons for extensive transfer of state-of-the-art technologies coming from the areas of quantum electronics, lasers and electro-optics to high-precision wireless detection and sensing. Lidars have developed so fast that they are becoming the key sensors in applications including autonomous vehicles, augmented reality (AR) and virtual reality (VR), etc. Photonic radars achieve dramatically improved performance over traditional radars, enabling high resolution and fast detection in all-day and all-weather conditions. New trends of lidar and photonic radar have attracted much attention such as AI-enhanced system, lidar-radar fusion system and single-photon lidar and so on.

Optical Crystals for 1.3 μm All-Solid-State Passively Q-Switched Laser

In recent years, optical crystals for 1.3 μm all-solid-state passively Q-switched lasers have been widely studied due to their eye-safe band, atmospheric transmission characteristics, compactness, and low cost. They are widely used in the fields of high-precision laser radar, biomedical applications, and fine processing. In this review, we focus on three types of optical crystals used as the 1.3 μm laser gain media: neodymium-doped vanadate (Nd:YVO4, Nd:GdVO4, Nd:LuVO4, neodymium-doped aluminum-containing garnet (Nd:YAG, Nd:LuAG), and neodymium-doped gallium-containing garnet (Nd:GGG, Nd:GAGG, Nd:LGGG). In addition, other crystals such as Nd:KGW, Nd:YAP, Nd:YLF, and Nd:LLF are also discussed. First, we introduce the properties of the abovementioned 1.3 μm laser crystals. Then, the recent advances in domestic and foreign research on these optical crystals are summarized. Finally, the future challenges and development trend of 1.3 μm laser crystals are proposed. We believe this review will provide a comprehensive understanding of the optical crystals for 1.3 μm all-solid-state passively Q-switched lasers.

Optimized impedance matching of micro-porous silicon dioxide coated graphite nanosheets as remarkable microwave absorber

Despite the high electromagnetic wave (EMW) attenuation capabilities, graphite nanosheets (GNs) are still facing great challenges as absorbing material owing to the strong reflection. Herein, a porous silicon dioxide coated GNs (PSi@GNs) is designed by sol–gel reaction to optimize the impedance matching of GNs. The coated porous SiO2 can not only balance the impedance between GNs and air, preventing the EMW reflection, but also increase the interfacial polarization behavior and benefit polarization loss. As a result, the as-synthesized SiO2/GNs exhibits superior EMW absorption performances with the minimum reflection loss (RL) value of −59.835 dB and effective absorbing bandwidth (EAB) of 3.36 GHz at 3.22 mm (25% filled). The advantages of the PSi@GNs potential applications in military radar, electronic communication, weapons and other fields.

Human Health Risk Assessment of 4-12 GHz Radar Waves using CST STUDIO SUITE Software.

Background: The application of radar systems in telecommunications and aerospace science is important. However, engineering department’s staff various tissues are always under chronic radiation generated by the radar fields which may affect health.Objective: This study aims to evaluate the risk of radar wave exposure and to explore the effects and limitations.Material and Methods: In this simulation study, an adult body model versus 1 watt source with a distance of 50 centimeters exposure has been simulated using the CST STUDIO SUITE. Furthermore, various physical and electrical properties of each tissue and organ for different frequencies and exposure times have been studied. The exposure dose limitations have been considered using the International Commission on Non-Ionizing Radiation Protection (ICNIRP) safety and health guide report.Results: Total body absorbed doses for 4 GHz, 8 GHz, and 12 GHz frequency, and 6 min, 4 h, and 30 days exposure time, have been calculated as 1.136×10-5, 1.598×10-5, 1.58×10-3, 1.521×10-5, 3.122×10-5, 4.52×10-3, 4.1×10-5, 10-4, and 10-2, respectively.Conclusion: It has shown that the internal organs of the body and head will be under more risk by reducing radar frequencies from 12 GHz to 4 GHz. On the other hand, the higher frequency can cause a higher risk to the human skin. In addition, the maximum Specific Absorption Rate (SAR) for each case has been calculated. The results show that for this normalized source, the safety criteria have been respected, but for a higher source, the calculations must be repeated.

Transition of Large‐Scale Environmental Conditions and Characteristics of Four Rainfall Types Observed by S‐PolKa During the MJO‐1 Active Phase of DYNAMO/CINDY/AMIE

AbstractAnalyses of National Center for Atmospheric Research (NCAR) S‐PolKa dual‐polarization radar data and ERA5 reanalysis fields indicate gradual changes in convection characteristics and large‐scale environmental conditions during a central Indian Ocean Madden‐Julian Oscillation (MJO) event observed by the DYNAMO/CINDY/AMIE field campaigns in late October 2011 (MJO‐1). Examination of four rainfall types (isolated convective cores, convective, mixed, and stratiform) reveals a transition of convection characteristics (i.e., areal coverage and depth) between distinct 5‐day environmental periods at the beginning and end of this ∼2‐week MJO active phase. A shift toward less frequent rainfall covering less of the radar domain for all four rainfall types occurs when large‐scale lower‐tropospheric dry air advects into the region with the westerly wind burst (WWB). Drier and warmer lower‐free‐tropospheric conditions associated with the WWB contribute to increased large‐scale surface‐based convective inhibition (CIN), surface‐based convective available potential energy (CAPE), and cloud base heights. A thermodynamic budget analysis indicates reduced surface heat fluxes contribute to the increased surface‐based CAPE. Greater CAPE at the end of MJO‐1 coincides with deeper 50‐dBZ convective echoes, while decreased 10‐dBZ depth for all rainfall types corresponds in time with WWB‐related dry‐air advection. Increased (decreased) reflectivity values in the lower‐level vertical reflectivity distribution of convective (stratiform) precipitation indicate increased (decreased) convective (stratiform) intensities when the WWB is present. The opposite depth changes for convective echoes and opposite shifts in convective and stratiform precipitation intensities underscore how the WWB can have differing impacts at different reflectivity thresholds and stages of the deep convection lifecycle.

Исследование мезомасштабной конвективной системы в центральных районах ЕТР 7 августа 2021 года

The article provides a case study of the formation of a severe convective storm as part of a quasilinear mesoscale convective system on August 7, 2021 that moved over the territory of the central part of European Russia. The predicted fields of meteorological characteristics and the following data of remote sensing of the atmosphere were analyzed: radar, satellite, lightning detection and atmospheric electric field data. A number of features were revealed in the distribution of meteorological parameters, which are useful for nowcasting of hazards associated with the formation of stable severe convective structures. Keywords: mesoscale convective system, severe convective storm, satellite and lightning detection signatures, instability indices, convective hazards, nowcasting, atmospheric electric field

Research on the SDIF Failure Principle for RF Stealth Radar Signal Design

Radio frequency (RF) stealth is one of the essential research hotspots in the radar field. The anti-sorting signal is an important direction of the RF stealth signal. Theoretically speaking, the anti-sorting signal design is based on the failure principle of the radar signal sorting algorithm, and the SDIF algorithm is a core sorting algorithm widely used in engineering. Thus, in this paper, the SDIF algorithm is first analyzed in detail. It is pointed out that the threshold function of the SDIF algorithm will fail when the signal pulse repetition interval (PRI) value obeys the interval distribution whose length is 20 times larger than the minimum interval of PRI. Secondly, the correctness of the failure principle of SDIF threshold separation is proved by the formula. Finally, the correctness is further verified by the signal design case. The principle of SDIF sorting threshold failure provides theoretical support for anti-sorting RF stealth signal design. It also complements the shortcoming of the casual design for the anti-sorting signal. Furthermore, the principle of SDIF sorting threshold failure helps improve anti-sorting signal design efficiency. Compared with the Dwell & Switch (D&S) signal and jitter signal, the anti-sorting ability of the signal designed by using the sorting failure principle is notably enhanced through simulation and experimentation.

Research and exploration on the interpretability of deep learning model in radar image

Although deep learning technology has been partially applied in radar image processing, the lack of interpretability analysis and comprehensive performance evaluation of black-box models limits the application performance, credibility, and universality of this technology in the radar image field. Starting from the interpretability, this paper proposes the mechanism analysis idea of deep learning black-box model in the radar image field and carries out experimental verification on the open-source MSTAR radar image dataset. It analyzes the transfer and cognitive mechanisms of the deep learning model and draws significant findings on transfer learning, attribution method application, and model robustness evaluation, filling gaps in the existing literature.

Development of a rangefinding method for determining the coordinates of targets by a network of radar stations in counter-battery warfare

The increase in the accuracy of determining the coordinates of targets is explained by the use of a network of counter-battery radar stations and the rangefinding method for determining the coordinates of targets. The main advantage of using the rangefinding method for determining the coordinates of targets in a network of counter-battery radar stations is to ensure the required accuracy in determining the coordinates of targets without using accurate measurement of angular coordinates. The minimum geometry of the system, which ensures the use of the rangefinding method for determining coordinates, is given. The method of determining the coordinates of targets by a network of counter-battery radar stations has been improved. In contrast to the known ones, information about the range to the target is additionally used in a spatially distributed network of radar stations for counter-battery combat. The boundaries of the working zones of the network of two and three counter-battery radar stations are calculated. The features of creating a continuous strip using the rangefinding method for determining the coordinates of the target are considered. Statistical modeling of the rangefinding method for determining the plane coordinates of the target has been carried out.
 It has been established that the use of the rangefinding method ensures the determination of the planar coordinates of the target in a sector of at least 120°. The targets are at a distance of direct radio visibility in relation to the counter-battery radar. The root-mean-square error in determining the target range in this case is no more than 50 m. It has been established that the creation of continuous bands of a low-altitude radar field at a certain height is possible by arranging radar stations in a line. In this case, the distance between the counter-battery radar stations should be no more than half the target detection range at this height

Influence of stealth aircraft dynamic RCS peak on radar detection probability

For modern stealth aircraft, it is important to analyze the influence of Radar Cross Section (RCS) peak exposure on penetration for guiding stealth design and penetration trajectory planning, which needs to reflect the RCS statistical uncertainty and the RCS difference with the change of incident angle. Based on the RCS characteristics of typical stealth aircraft, this paper established a simplified RCS dynamic fluctuation statistical model with the parameters log mean and log standard deviation. According to the detection probability algorithm in radar signal processing field, this paper built the algorithm of radar detection probability based on the RCS dynamic fluctuation statistical model. The analysis of examples concluded that the key to successful penetration is to shorten the RCS peak exposure time, which can be reduced by decreasing the RCS peak width or increasing velocity. Based on the conclusion, this paper proposed the method of turning maneuvering to reduce RCS peak exposure time dramatically.

Multianticipation for string stable Adaptive Cruise Control and increased motorway capacity without vehicle-to-vehicle communication

Adaptive Cruise Control (ACC) systems have been expected to solve many problems of motorway traffic. Now that they are widespread, it is observed that the majority of existing systems are string unstable. Therefore, small perturbations in the speed profile of a vehicle are amplified for the vehicles following upstream, with negative impacts on traffic flow, fuel consumption, and safety. Increased headway settings provide more stable flow but at the same time it deteriorates the capacity. Substantial research has been carried out in the past decade on utilizing connectivity to overcome this trade-off. However, such connectivity solutions have to overcome several obstacles before deployment and there is the concrete risk that motorway traffic flow will considerably deteriorate in the meanwhile. As an alternative solution, the paper explores multianticipation without inter-vehicle communication, taking advantage of the recent advancements in the field of RADAR sensing. An analytical study is carried out, based on the most widely used model and parameter settings used to simulate currently available commercial ACC systems, comparing the transfer functions and step responses for the nominal and the multianticipative formulations. Then, a microsimulation framework is employed to validate our claim on different speed profiles. Analytical results demonstrate that multianticipation enhances stability without impacting traffic flow. On the contrary, the simulation study indicates that the multianticipative-ACC can produce higher road capacity even in the presence of external disturbances and for a wide range of calibrated parameters. Finally, optimality conditions for the tuning of the headway policy are derived from a Pareto optimization.

Adaptive Max-Margin One-Class Classifier for SAR Target Discrimination in Complex Scenes

Synthetic aperture radar (SAR) target discrimination is an important stage that distinguishes targets from clutters in the radar automatic target recognition field. However, in complex SAR scenes, the performance of some traditional discriminators will degrade. As an effective tool for one-class classification (OCC), the max-margin one-class classifier has attracted much attention for SAR target discrimination, as it can effectively reduce the impact of multiple clutters. However, the performance of the max-margin one-class classifier is very sensitive to the values of kernel parameters. To solve the problem, this paper proposes an adaptive max-margin one-class classifier for SAR target discrimination in complex scenes. In a max-margin one-class classifier with a suitable kernel parameter, the distance between a sample and classification boundary satisfies a certain geometric relationship, i.e., edge samples in input space are transformed to the region in the kernel space close to boundary, while interior samples in input space are transformed to the region in the kernel space far away from boundary. Therefore, we define the information entropy of samples in the kernel space to measure the distance between samples and classification boundary. To automatically obtain the optimal kernel parameter of the max-margin one-class classifier, the edge and interior samples in the input space are first selected, and then the parameter optimization is performed by minimizing information entropy of interior samples and simultaneously maximizing the information entropy of edge samples. Experimental results of the synthetic datasets and measured synthetic aperture radar (SAR) datasets validate the effectiveness of our method.

Effects of cerium doping on the microstructure, mechanical properties, thermal conductivity, and dielectric properties of ZrP2O7 ceramics

A two-step method, combined with cold isostatic pressing, was used to prepare CeO2-doped ZrP2O7 ceramics, and their microstructure, mechanical properties, thermal conductivities, and dielectric properties were determined. It was found that CeO2 doping could increase the Zr–P and P–O bond lengths, which in turn decreased the thermal conductivity of the ZrP2O7 matrix. Doping with 12 wt% CeO2 simultaneously reduced the sintering temperature and improved the mechanical properties of the ZrP2O7 ceramics, while retaining its low thermal conductivity and good dielectric properties. The maximum cold modulus of rupture of a sample at 1250 °C was 75.91 MPa, which met most conditions for use at room temperature. A COMSOL model was used to predict the thermal conductivity, based on the microstructure, with a relatively high degree of accuracy. The thermal conductivity of the CeO2-doped samples was lower than 1.083 W/(m·K). The dielectric constant was in the range of 5.93–6.52 at 20–40 GHz, and the dielectric loss was less than 4 × 10−3. The ZrP2O7-doped ceramics have potential for application in millimetre wave technology, satellite communication, and vehicle radar fields, because they can meet the high thermal insulation requirements for these applications.

Evaluation of landslide susceptibility of the Ya’an–Linzhi section of the Sichuan–Tibet Railway based on deep learning

The Qinghai–Tibet Plateau is an area with frequent landslide hazards due to its unique geology, topography, and climate conditions, posing severe threats to engineering construction and human settlements. The primary purpose of this paper is to map the landslide susceptibility of the Ya’an–Lin branch of the Sichuan–Tibet Railway using two deep learning (DL) algorithms, convolutional neural network (CNN) and deep neural network (DNN). Initially, a geospatial database was generated based on 587 landslide hazards determined by Interferometric Synthetic Aperture Radar (InSAR) Stacking technology and field geological hazard surveys; thus, 18 landslide-influencing factors were selected. Subsequently, the landslides were randomly divided into training (70%) and validation data (30%) for model training and testing. Next, a Pearson correlation coefficient and information gain (IG) method were used to perform the correlation analysis and feature selection of the 18 influencing factors. Afterward, landslide susceptibility maps were generated for the two models. Finally, the performance of the model is validated using the receiver-operating characteristic (ROC) curve and confusion matrix. The results show that the CNN model (AUC = 0.88) provided better performance in both the training and testing phases compared to the DNN model (AUC = 0.84). In addition, the high landslide susceptibility is primarily distributed in the Jinsha, Lancang and Nu River basins along the railway. The slope, altitude and rainfall are the main factors for the formation of the landslides. Furthermore, the two deep learning models can accurately map the landslide susceptibility, providing important information for landslide risk reduction and prevention.

A Deep Learning Based Super Resolution DoA Estimator With Single Snapshot MIMO Radar Data

In this article, we study the radar angular resolution, the capability of distinguishing multiple targets in different directions of arrivals (DoA). We present a deep learning-based super resolution DoA estimator for multiple input multiple output (MIMO) radar with single snapshot data. The estimator consists of a deep learning DoA classifier (DLDC) in the central bearing angle zone, which can simultaneously detect up to 11 targets in <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$[ { - 2^\circ,\ 2^\circ } ]$</tex-math></inline-formula> with 32 virtual antenna elements, and a spatial filtering pre-rotator (SFPR) that makes the DLDC supporting any bearing angle location in a wider radar field of view (FoV). We present the DLDC-SFPR structure and provide detailed parameter setting. In the performance evaluation, the resolution of proposed method is first compared with the theoretical angular resolution limit (ARL). It is shown that the proposed estimator reaches the Chernoff ARL. The probability of resolution (PoR) is then investigated and compared with typical and state-of-the-art DoA estimators in various conditions. The numerical results show that the DLDC-SFPR achieves 0.4 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">°</sup> resolution with 90% PoR to distinguish 2 targets at signal to noise ratio (SNR) of 7 dB if the targets are on-gird. In the case of any-angle, the same resolution and probability are achieved at SNR = 12 dB. The proposed method is robust to multi-target concurrent cases, and outperforms the existing reported approaches. Two procedures, uniform scanning and progressive scanning, are proposed for wider FoV applications.

A low complexity STPAP algorithm based on an alternating polarization-sensitive array

BackgroundSpace–time adaptive processing (STAP) has been widely used in the fields of communication, radar, and navigation anti-jamming. However, the traditional scalar arrays used with STAP have limitations because they can only obtain spatial information. To improve the performance, joint space–time filtering is proposed using an alternating polarization-sensitive array (APSA). Compared to a dual-polarization sensitive array (DPSA), this array can provide polarization information and reduce the computational complexity.MethodsAn alternating polarization-sensitive array space–time polarization adaptive processing (APSA-STPAP) algorithm is proposed based on the linear constraint minimum variance (LCMV) criterion. Different from the traditional LCMV criterion, the space–time polarization joint steering vector of the desired and interference signals is used as the constraint matrix, and the “set 1” and “set 0” conditions are used as the constraint conditions to effectively suppress the interference signals and enhance the desired signals.ResultsSimulation results are presented which show the following. (1) Filtering with the proposed APSA-STPAP algorithm is similar to that with the DPSA-STPAP algorithm. From the perspective of the spatial, time, and polarization domains, it can form nulls in the directions of the interference and realize space–time-polarization adaptive processing. (2) The APSA-STPAP algorithm has lower computational complexity than the DPSA-STPAP algorithm. Moreover, the dipole of the alternating polarization sensitive array is halved, which reduces the coupling effect between electric dipoles and makes implementation easier. (3) The APSA-STPAP algorithm maintains good anti-interference performance even when the electric dipole and anti-jamming degrees of freedom are reduced by half, and the anti-jamming performance is similar to that of a polarization-sensitive array. There is little difference between the anti-interference performance of the APSA-STPAP and DPSA-STPAP algorithms when SNR ≥ − 10 dB.

Parameter Estimation for Uniformly Accelerating Moving Target in the Forward Scatter Radar Network

Passive radar based on the global navigation satellite positioning system (GNSS) has become the focus of attention in the field of radar. A parameter estimation method is proposed in the forward scatter radar (FSR) network based on GNSS to extend the application scenarios. For uniformly accelerating moving targets, only the instant times when the target crosses the individual baselines are used to retrieve the target motion parameters. The target position, velocity, and acceleration information can be obtained. Firstly, the minimum network configuration is derived theoretically. Then, the effects of crossing time error, station location error, transmitting/receiving station deployment, and target height on the accuracy are analyzed through Monte Carlo simulations. Finally, the simulation results indicate that the target position estimation error is in the order of 100 m. This paper provides the fundamental theory of aerial target positioning with a GNSS-based FSR network.