Radar Research Articles

Weather Forecasting

My Project is on implementation of a weather forecasting using python and machine learning. real-time weather data from locations across the world. Through Python scripting, the project generates accurate forecasts for diverse regions, providing essential meteorological parameters such as temperature, wind speed, humidity, and weather conditions. Weather forecasting plays a crucial role in various aspects of human life, from planning outdoor activities to making strategic decisions in sectors like agriculture, transportation, and emergency management. In this project, we explore the application of Python programming language in weather forecasting, employing both open-source libraries and APIs to access and analyze weather data. Through this project, we aim to demonstrate the versatility of Python in harnessing weather data for forecasting purposes, showcasing its capabilities in data retrieval, analysis, visualization, and predictive modelling. By empowering users with accessible tools and methodologies, we envision a future where weather forecasting becomes more accurate, reliable, and actionable, contributing to informed decision-making and enhanced societal resilience in the face of changing weather patterns. Weather forecasting is the scientific process of predicting atmospheric conditions at a specific location and time. It involves the collection and analysis of data from various sources, such as satellites, weather stations, and radar systems. This data is then input into numerical weather prediction models, which use mathematical equations to simulate the behavior of the atmosphere. Advances in technology, such as high-performance computing and machine learning, have significantly improved the accuracy and reliability of weather forecasts. These predictions are crucial for a wide range of applications, including agriculture, disaster management, transportation, and daily planning, helping to mitigate the impacts of severe weather events and enhance societal preparedness and safety. Despite significant progress, challenges remain due to the inherently chaotic nature of the atmosphere, which limits the precision of long-term forecasts

Determining the parameters of a dielectric ellipsoid for minimising front echo in Gaussian pulse scattering analysis

AbstractA novel numerical method is introduced for minimising the presence of scattering echoes in the frontal area of a dielectric ellipsoid through the application of the method of auxiliary sources (MAS) method. This approach utilises a Gaussian radio pulse that spans the frequency spectrum from 0 to 6 GHz. The approach involves determining the properties of an ellipsoid, such as its dimensions and a dielectric permittivity, using a specially developed MATLAB software package to make this ellipsoid invisible. This provides a valuable military and defence technology tool, particularly for stealth and invisibility technology applications. It is beneficial for avoiding detection by radar systems utilised for enemy surveillance and identifying concealed objects. This aims to eliminate the need for metamaterials for invisibility cloaking. The suggested methodology has been confirmed through a validation process, which involved a comparison of the outcomes derived from numerical experiments conducted during pulse echo observations using the FDTD method and an analytical solution. MAS offers a notable advantage over other methods and commercial software by significantly reducing computational time for simulating and analysing 3D dielectric objects. This allows for quicker consideration of various parameter scenarios and optimisation for desired outcomes.

Radar-Enabled non-contact speed estimation for rotating electrical machinery

Electric motors play a pivotal role in contemporary industrial processes, prompting the necessity for their continuous monitoring through an array of sensors in various applications. Among the crucial parameters under scrutiny, the operational speed of electric motors holds a prominent position. Current methods for speed detection conventionally entail the utilization of physically connected sensors or the measurement of some electrical variables. Nonetheless, a non-contact speed measurement approach with a mobile device promises low cost, efficiency, and even safety in some specific cases. This research delves into the remote speed detection of electric motors employing Continuous Wave radar technology. To this end, phase extraction techniques, which have demonstrated robust performance in diverse application domains, have been assessed for their efficacy in motor speed detection. First, five distinct methods have been utilized to extract the phase of baseband signals obtained from a 24 GHz IQ-demodulated radar system directed toward an asynchronous motor. These methodologies have undergone comprehensive evaluation in experimental measurements and simulation encompassing ideal and noisy conditions. Complex Signal Demodulation, one of the phase extraction methods, exhibits a noteworthy superiority over alternative approaches in motor speed measurement experiments. These experiments have entailed three different load scenarios and encompassed ten distinct speeds, equally spaced between 500 and 1400 rpm. The Complex Signal Demodulation method has delivered exceptional results, successfully detecting motor speeds across thirty experiments with a mean absolute percentage error of merely 0.08. Radar-based non-contact speed detection holds immense promise, particularly in fault diagnosis and predictive maintenance.

RCS-reduced patch antenna with wide frequency tuning range based on absorbing metasurface

A frequency-tunable patch antenna with reduced radar cross section (RCS) based on absorbing metamaterial is proposed. By controlling the external voltage of the varactor diodes, the working frequency of the patch can be adjusted. Through an investigation of the composite structure of the patch and absorbing metamaterial, RCS reduction is achieved over the entire wide tunable frequency range of the antenna. Measured results illustrate that the proposed patch antenna can dynamically operate in the range from 2.28 to 3.6 GHz with a gain variation of 7.1–8.7 dBi. Meanwhile, the RCS can be reduced by at least 7 dB and up to 20 dB in a fractional bandwidth of 44.9% of the antenna under a normal incidence. In comparison to conventional RCS-reduced antennas, the proposed one features a broader frequency tuning range and has potential value in wideband radar systems under complex electromagnetic interference environments.

A tristatic phased array radar system in China

A tristatic phased array radar system in China

Performance Analysis of Moving Target Shadow Detection in Video SAR Systems

The video synthetic aperture radar (ViSAR) system can utilize high-frame-rate scene motion target shadow information to achieve real-time monitoring of ground mobile targets. Modeling the characteristics of moving target shadows and analyzing shadow detection performance are of great theoretical and practical value for the optimization design and performance evaluation of ViSAR systems. Firstly, based on the formation mechanism and characteristics of video SAR moving target shadows, two types of shadow models based on critical size and shadow clutter ratio models are established. Secondly, for the analysis of moving target shadow detection performance in ViSAR systems, parameters such as the maximum detectable speed of moving targets, the minimum clutter backscatter coefficient, and the number of effective shadow pixels of moving targets are derived. Furthermore, the shadow characteristics of five typical airborne/spaceborne ViSAR systems are analyzed and compared. Finally, a set of simulation experiments on moving target shadow detection for the Hamas rocket launcher validates the correctness and effectiveness of the proposed models and methods.

BmmW: A DNN-based joint BLE and mmWave radar system for accurate 3D localization with goal-oriented communication

Bluetooth Low Energy (BLE) has emerged as one of the reference technologies for the development of indoor localization systems, due to its increasing ubiquity, low-cost hardware, and to the introduction of direction-finding enhancements improving its ranging performance. However, the intrinsic narrowband nature of BLE makes this technology susceptible to multipath and channel interference. As a result, it is still challenging to achieve decimetre-level localization accuracy, which is necessary when developing location-based services for smart factories and workspaces. To address this challenge, we present BmmW, an indoor localization system that augments the ranging estimates obtained with BLE5.1’s constant tone extension feature with mmWave radar measurements to provide 3D localization of a mobile tag with decimetre-level accuracy. Specifically, BmmW embeds a deep neural network (DNN) that is jointly trained with both BLE and mmWave measurements, practically leveraging the strengths of both technologies. In fact, mmWave radars can locate objects and people with decimetre-level accuracy, but their effectiveness in monitoring stationary targets and multiple objects is limited, and they also suffer from a fast signal attenuation limiting the usable range to a few meters. We evaluate BmmW’s performance experimentally, and show that its joint DNN training scheme allows to track mobile tags with a mean 3D localization accuracy of 10 cm when combining angle-of-arrival BLE measurements with mmWave radar data. We further assess two variations of BmmW: BmmW-Lite and BmmW-Lite+, both tailored for single-antenna BLE devices, which eliminates the necessity for bulky and expensive multi-antenna arrays and represents a cost-effective solution that is easy to integrate into compact IoT devices. In contrast to classic BmmW (which utilizes angle-of-arrival info), BmmW-Lite uses raw in-phase/quadrature (I/Q) measurements, and achieves a mean localization accuracy of 36 cm, thus facilitating precise object tracking in indoor environments even when using budget-friendly single-antenna BLE devices. BmmW-Lite+ extends BmmW-Lite by allowing the localization task to be transferred from the edge to the cloud due to device memory and power constraints. To this end, BmmW-Lite+ employs a goal-oriented communication paradigm that compresses initial BLE features into a more compact semantic format at the edge device, which allows to minimize the amount of data that needs to be sent to the cloud. Our experimental results show that BmmW-Lite+ can compress raw BLE features by up to 12% of their initial size (hence allowing to save network bandwidth and minimize energy consumption), with negligible impact on the localization accuracy.

Long-Time Coherent Integration for the Spatial-Based Bistatic Radar Based on Dual-Scale Decomposition and Conditioned CPF

This paper addresses the problem of weak maneuvering target detection in the space-based bistatic radar system through long-time coherent integration (LTCI). The space-based bistatic radar is vulnerable to the high-order range migration (RM) and Doppler frequency migration (DFM), since the target, the receiver and the transmitter all can play fast movement independently. To correct high- order RM and DFM, this usually involves joint high-dimensional parameter searching, incurring a large computational burden. In our previous work, a dual-scale (DS) decomposition of motion parameters was proposed, in which the optimal GRFT is conditionally decoupled into two cascade procedures called the modified generalized inverse Fourier transform (GIFT) and generalized Fourier transform (GFT), resulting in the DS-GRFT detector. However, even if the DS-GRFT detector preserves the superior performance and dramatically decreases the complexity, high-dimensional searching is still required. In this paper, by analyzing the structure of the DS-GRFT detector, we further designed a conditioned cubic phase function (CCPF) tailored to the range–slow-time signal after GIFT, breaking the joint high-dimensional searching into independent one-dimensional searching. Then, by connecting the proposed CCPF with the GIFT, we achieved a new LTCI detector called the DS-GIFT-CCPF detector, which obtained a significant computational cost reduction with acceptable performance loss, as demonstrated in numerical experiments.

Emerging Technologies for Remote Sensing of Floating and Submerged Plastic Litter

Most advances in the remote sensing of floating marine plastic litter have been made using passive remote-sensing techniques in the visible (VIS) to short-wave-infrared (SWIR) parts of the electromagnetic spectrum based on the spectral absorption features of plastic surfaces. In this paper, we present developments of new and emerging remote-sensing technologies of marine plastic litter such as passive techniques: fluid lensing, multi-angle polarimetry, and thermal infrared sensing (TIS); and active techniques: light detection and ranging (LiDAR), multispectral imaging detection and active reflectance (MiDAR), and radio detection and ranging (RADAR). Our review of the detection capabilities and limitations of the different sensing technologies shows that each has their own weaknesses and strengths, and that there is not one single sensing technique that applies to all kinds of marine litter under every different condition in the aquatic environment. Rather, we should focus on the synergy between different technologies to detect marine plastic litter and potentially the use of proxies to estimate its presence. Therefore, in addition to further developing remote-sensing techniques, more research is needed in the composition of marine litter and the relationships between marine plastic litter and their proxies. In this paper, we propose a common vocabulary to help the community to translate concepts among different disciplines and techniques.

Sparse Reconstruction-Based Joint Signal Processing for MIMO-OFDM-IM Integrated Radar and Communication Systems

Multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) technology is widely used in integrated radar and communication systems (IRCSs). Moreover, index modulation (IM) is a reliable OFDM transmission scheme in the field of communication, which transmits information by arranging several distinguishable constellations. In this paper, we propose a sparse reconstruction-based joint signal processing scheme for integrated MIMO-OFDM-IM systems. Combining the advantages of MIMO and OFDM-IM technologies, the integrated MIMO-OFDM-IM signal design is realized through the reasonable allocation of bits and subcarriers, resulting in better intercarrier interference (ICI) resistance and a higher transmission efficiency. Taking advantage of the sparseness of OFDM-IM, an improved target parameter estimation method based on sparse signal reconstruction is explored to eliminate the influence of empty subcarriers on the matched filtering at the receiver side. In addition, an improved sequential Monte Carlo signal detection method is introduced to realize the efficient detection of communication signals. The simulation results show that the proposed integrated system is 5 dB lower in the peak sidelobe ratio (PSLR) and 1.5 ×105 lower in the number of complex multiplications than the latest MIMO-OFDM system and can achieve almost the same parameter estimation performance. With the same spectral efficiency, it has a lower bit error rate (BER) than existing methods.

Coseismic deformation and interseismic strain accumulation of the 2024 MS 7.1 Wushi earthquake in Xinjiang, China

On January 23, 2024, a magnitude 7.1 earthquake occurred in Wushi County, Aksu Prefecture, Xinjiang, China. The epicenter was located at the Maidan Fault, which is known for its left-lateral strike-slip and thrust characteristics within the Tianshan seismic zone. In this study, we used Interferometric Synthetic Aperture Radar (InSAR) and Global Navigation Satellite System (GNSS) observations to investigate the characteristics and mechanisms of both the coseismic and interseismic deformation. The main findings are as follows: 1) The InSAR coseismic deformation displays clear thrust characteristics, with a maximum relative displacement of ∼ 0.8 m along the line of sight, thereby indicating a thrust-type earthquake. 2) The majority of the slip during the earthquake occurred within a 60 km × 40 km area, with depths ranging from 4 to 26 km. The maximum slip of the fault was about 2.3 m, corresponding to a moment magnitude of 7.0. 3) The seismogenic faults primarily exhibit thrust-type movement characteristics during the interseismic period and possess a clearly defined locking state. The earthquake rupture depth aligns with the depth of locking during interseismic periods. 4) The seismogenic structure experienced compressive deformation in a north–south direction prior to the earthquake, with the epicenter located on the surroundings of the high-strain area, as indicated by the GNSS strain rate.

Virtual Antenna Arrays with Frequency Diversity for Radar Systems in Fifth-Generation Flying Ad Hoc Networks

This paper proposes the design of virtual antenna arrays with frequency diversity for radar systems in fifth-generation flying ad hoc networks. These virtual arrays permit us to detect targets from the sky with flying drones. Each array element is composed of a microstrip antenna mounted on quadcopter drones and is virtually connected with the other elements. The antennas are tuned to work at the lower fifth-generation frequency band of 3.5 GHz. The design process considers the optimization of frequency offsets and positions for each element to obtain a side lobe level reduction. This methodology is carried out by particle swarm optimization. Several design examples are presented with random frequency offsets and non-uniform positions. These designs are compared to uniform-spaced arrays excited with Hamming frequency offsets. The simulation results show that using random frequency offsets and non-uniform positions provides a minor side lobe level reduction. This research demonstrates the feasibility of using virtual arrays for radar systems in fifth-generation flying ad hoc networks.

Wideband Low Phase-Noise Signal Generation Using Coaxial Resonator in Cascaded Phase Locked Loop

The generation of high-quality wideband frequency sweeps presents a significant challenge, particularly in modern telecommunication, radar, and measurement systems where miniaturization is paramount. While phase-locked loops (PLLs) have become the dominant technique for signal generation, their application in broadband sweeps necessitates fractional-N operation. This, in turn, degrades phase noise and introduces unwanted spurs. This paper proposes a novel approach for broadband signal generation. By cascading two PLLs and utilizing a coaxial resonator, we achieve a high-performance oscillator that operates without the excessive fractional spurs, maintaining their level below −80 dBc across the entire frequency band. The prototype demonstrates non-degraded phase noise performance, reaching −102 dBc/Hz at 100 kHz offset and −121 dBc/Hz at 1 MHz offset for signals at 10 GHz. Despite significant frequency jumps, our design achieves lock times below 41 µs. These results, supported by theoretical analysis, validate the proposed method’s effectiveness in generating low-noise broadband frequency sweeps, ideal for local oscillator applications.

Implementation of MIMO Radar-Based Point Cloud Images for Environmental Recognition of Unmanned Vehicles and Its Application

High-performance radar systems are becoming increasingly popular for accurately detecting obstacles in front of unmanned vehicles in fog, snow, rain, night and other scenarios. The use of these systems is gradually expanding, such as indicating empty space and environment detection rather than just detecting and tracking the moving targets. In this paper, based on our high-resolution radar system, a three-dimensional point cloud image algorithm is developed and implemented. An axis translation and compensation algorithm is applied to minimize the point spreading caused by the different mounting positions and the alignment error of the Global Navigation Satellite System (GNSS) and radar. After applying the algorithm, a point cloud image for a corner reflector target and a parked vehicle is created to directly compare the improved results. A recently developed radar system is mounted on the vehicle and it collects data through actual road driving. Based on this, a three-dimensional point cloud image including an axis translation and compensation algorithm is created. As a results, not only the curbstones of the road but also street trees and walls are well represented. In addition, this point cloud image is made to overlap and align with an open source web browser (QtWeb)-based navigation map image to implement the imaging algorithm and thus determine the location of the vehicle. This application algorithm can be very useful for positioning unmanned vehicles in urban area where GNSS signals cannot be received due to a large number of buildings. Furthermore, sensor fusion, in which a three-dimensional point cloud radar image appears on the camera image, is also implemented. The position alignment of the sensors is realized through intrinsic and extrinsic parameter optimization. This high-performance radar application algorithm is expected to work well for unmanned ground or aerial vehicle route planning and avoidance maneuvers in emergencies regardless of weather conditions, as it can obtain detailed information on space and obstacles not only in the front but also around them.

STAM-LSGRU: a spatiotemporal radar echo extrapolation algorithm with edge computing for short-term forecasting

With the advent of Mobile Edge Computing (MEC), shifting data processing from cloud centers to the network edge presents an advanced computational paradigm for addressing latency-sensitive applications. Specifically, in radar systems, the real-time processing and prediction of radar echo data pose significant challenges in dynamic and resource-constrained environments. MEC, by processing data near its source, not only significantly reduces communication latency and enhances bandwidth utilization but also diminishes the necessity of transmitting large volumes of data to the cloud, which is crucial for improving the timeliness and efficiency of radar data processing. To meet this demand, this paper proposes a model that integrates a spatiotemporal Attention Module (STAM) with a Long Short-Term Memory Gated Recurrent Unit (ST-ConvLSGRU) to enhance the accuracy of radar echo prediction while leveraging the advantages of MEC. STAM, by extending the spatiotemporal receptive field of the prediction units, effectively captures key inter-frame motion information, while optimizations to the convolutional structure and loss function further boost the model’s predictive performance. Experimental results demonstrate that our approach significantly improves the accuracy of short-term weather forecasting in a mobile edge computing environment, showcasing an efficient and practical solution for processing radar echo data under dynamic, resource-limited conditions.

Inverse design of dual-bandpass metasurface filters empowered by the multi-objective genetic algorithm

Multi-band metasurface filters are becoming increasingly pivotal in high-capacity communication technologies. Traditional methods for designing metasurface structures, to date, have relied on empirical approaches to obtain target electromagnetic responses, thereby suffering from low efficiency. Here, we demonstrate an advanced approach for the inverse design of a multi-band metasurface filter, which consists of a multi-objective genetic algorithm (MOGA) in tandem with equivalent circuit model (ECM) analysis. This integration converts specific frequency response requirements into ECM parameter constraints, significantly streamlining the metasurface design and optimization process and offering superior solutions. Using this inverse design method, we theoretically propose a dual-bandpass metasurface filter which can exhibit transmission passbands in any two adjacent frequency ranges among the X-, Ku-, and K-bands. Further, numerical simulations validate the performances of the proposed device, which show great agreement with MOGA-based predictions. Our results pave the way to the effective inverse design of multi-passband metasurface filters which are useful in many applications, such as microwave filters, radar and satellite communication systems, and radio frequency identification devices.

Pressure Drop and Cavitation Optimization of a Relief Valve Featuring Quick Coupling Used in Radar Systems

AbstractThis extensive study is carried out to minimize the pressure drop of a relief valve featuring quick-releasing coupling used in radar systems. The 3D two-phase computational fluid dynamic (CFD) study is verified with the experiments, and Taguchi’s orthogonal method is implemented for pressure drop optimization. Realizable k–ε turbulence model with enhanced wall treatment and the Schnerr and Sauer cavitation model are enforced within the numerical study. Four angles—the collet angle, the inlet angle, the plug angle, and the outlet angle—are considered at five levels of variation for the orthogonal optimization process. According to the statistical analysis, two more designs are suggested, and one of them resulted in further improved pressure drop performance. Moreover, the cavitation behavior of the proposed design is compared with the base design through water vapor volume fractions. While the water vapor volume fraction of the base design is 0.0045, no vapor formation is observed in the optimized designs at 20 l/min. The results of the proposed design are also validated by the experiments. The outcomes of the study showed that the inlet angle has a significant effect on the pressure drop phenomenon.

Laser-assisted screen-printing of graphene folded reflectarray antenna for millimeter-wave applications

Antennas operating at high frequency bands are sought after for applications in 5G, satellite, radar, and other communication systems. Graphene, a two-dimensional material known for its exceptional conductivity, mechanical strength, and chemical stability, is anticipated to serve as a viable substitute for copper in antenna applications. However, researches on graphene antennas at millimeter-wave and higher frequencies are mainly limited to theory and simulation, due to the fabrication challenges of achieving high-precision graphene patterns. Herein, we present the design and fabrication of a graphene folded reflectarray antenna (FRA) operating at millimeter-wave. The FRA is fabricated using screen-printing of graphene ink on an RT5880 substrate, followed by laser engraving for precise patterning. The main reflector has 918 patch units, and the polarizing grids have line widths and spacings of 100 μm. Measurements demonstrate that the antenna has a realized peak gain of 21.37 dBi at 37.5 GHz. The radiation patterns show a 3 dB beamwidth of 6° and 4° in the E-plane and H-plane respectively, with cross-polarization more than −18 dB. The laser-assisted screen-printing method provides a feasible strategy for the precision manufacture of graphene communication devices.

Dual-Polarization Conversion and Coding Metasurface for Wideband Radar Cross-Section Reduction

Modern stealth application systems require integrated meta-devices to operate effectively and have gained significant attention recently. This research paper proposes a 1-bit coding metasurface (CM) design. The fundamental component of the proposed CM is integrated to convert linearly polarized incoming electromagnetic waves into their orthogonal counterpart within frequency bands of 12.37–13.03 GHz and 18.96–32.37 GHz, achieving a polarization conversion ratio exceeding 99%. Furthermore, it enables linear-to-circular polarization conversion from 11.80 to 12.29, 13.17 to 18.44, and 33.33 to 40.35 GHz. A second element is produced by rotating a fundamental component by 90°, introducing a phase difference of π (pi) between them. Both elements are arranged in an array using a random aperiodic coding sequence to create a 1-bit CM for reducing the radar cross-section (RCS). The planar structure achieved over 10 dB RCS reduction for polarized waves in the frequency bands of 13.1–13.8 GHz and 20.4–30.9 GHz. A prototype was fabricated and tested, with the experimental results showing a good agreement with the simulated outcomes. The proposed design holds potential applications in radar systems, reflector antennas, stealth technologies, and satellite communication.

Dual-Frequency, Dual-Mode Reconfigurable Digital Atmospheric Radar Receiver Design

A new dual-frequency, dual-mode reconfigurable digital receiver based on Field-Programmable Gate Array (FPGA) dynamic reconfiguration is proposed, which is based on a common hardware platform of high-bandwidth RF front-end, high-speed data acquisition, and real-time signal processing. The receiver adopts the design of dynamically reconfigurable down-conversion, filter extraction, and matched filtering in the digital domain. In this study, we completed the design and development of the digital receiver, experimental platform construction, and field detection test with hardware and software cooperation. The experimental results show that the receiver achieves full digital reception and signal processing for 53.8 MHz stratosphere–troposphere (ST) detection and 35.0 MHz meteor detection and successfully acquired the number of meteors versus time, the meteor trail, and low-altitude atmospheric radial winds. This dual-frequency, dual-mode reconfigurable digital receiver can be applied to new-generation multifunction integrated radar systems such as dual-frequency ST/meteor radars.