Search Results (124)

Every day, millions of meteoroids enter the atmosphere and ablate, forming a long plasma trail. It is a strongly scattering object for electromagnetic waves and can be effectively detected by meteor radar at altitudes between 70 km and 140 km. Its echo typically has Fresnel oscillation characteristics. Most of the traditional detection methods rely on determining the threshold value of the signal-to-noise ratio (SNR) and solving parameters to recognize meteor echoes, making them highly susceptible to interference. In this paper, a neural network model, YOLOv8n-BP, was proposed for detecting the echoes of underdense meteors by identifying them from their echo characteristics. The model combines the strengths of both YOLOv8 and back propagation (BP) neural networks to detect underdense meteor echoes from Range-Time-Intensity (RTI) plots where multiple echoes are present. In YOLOv8, the n-type parameter represents the lightweight version of the model (YOLOv8n), which is the smallest and fastest variant in the YOLOv8 series, specifically designed for resource-constrained scenarios. Experiments show that YOLOv8n has excellent recognition ability for underdense meteor echoes in RTI plots and can automatically extract underdense meteor echoes without the influence of radio-frequency interference (RFI) and disturbance signals. Limited by the labeling error of the dataset, YOLOv8 is not precise enough in recognizing the head and tail of meteors in the radar echograms, which may result in the extraction of imperfect echoes. Utilizing the Fresnel oscillation properties of meteor echoes, a BP network based on a Gaussian activation function is designed in this paper to enable it to detect meteor head and tail positions more accurately. The YOLOv8n-BP model can quickly and accurately detect and extract underdense meteor echoes from RTI plots, providing correct data for meteor parameters such as radial velocities and diffusion coefficients, which are used to allow wind field calculations and estimate atmospheric temperature. Full article

The intense rising of machine learning in the previous years, bolstered by post-COVID-19 digitalization, left some of us pondering upon the transparency practices involving projects sourced from European Union funds. This study focuses on the European Union research clusters and trends in the ecosystem of higher education institutions (HEIs). The manually curated dataset of bibliometric data from 2020 to 2024 was analyzed in steps, from the traditional bibliometric indicators to natural language processing and collaboration networks. Centrality metrics, including degree, betweenness, closeness, and eigenvector centrality, and a three-way-intersection of community detection algorithms were computed to quantify the influence and the connectivity of institutions in different communities in the collaborative research networks. In the EU context, results indicate that institutions such as Universidad Politecnica de Madrid, the University of Cordoba, and Maastricht University frequently occupy central positions, echoing their role as local or regional hubs. At the global level, prominent North American and UK-based universities (e.g., University of Pennsylvania, Columbia University, Imperial College London) also remain influential, standing as a witness to their enduring influence in transcontinental research. Clustering outputs further confirmed that biomedical and engineering-oriented lines of inquiry often dominated these networks. While multiple mid-ranked institutions do appear at the periphery, the data highly implies that large-scale initiatives gravitate toward well-established players. Although the recognized centers provide specialized expertise and resources, smaller universities typically rely on a limited number of niche alliances. Full article

Traditional single-channel Synthetic Aperture Radar (SAR) cannot achieve high-resolution and wide-swath (HRWS) imaging due to the constraint of the minimum antenna area. Distributed HRWS SAR can realize HRWS imaging and also possesses the resolution ability in the height dimension by arranging multiple satellites in the elevation direction. Nevertheless, due to the excessively high pulse repetition frequency (PRF) of the distributed SAR system, range ambiguity will occur in large detection scenarios. When directly performing 3D-imaging processing on SAR images with range ambiguity, both focused point clouds and blurred point clouds will exist simultaneously in the generated 3D point clouds, which affects the quality of the generated 3D-imaging point clouds. To address this problem, this paper proposes a 3D blur suppression method for HRWS blurred images, which estimates and eliminates defocused point clouds based on focused targets. The echoes with range ambiguity are focused in the near area and the far area, respectively. Then, through image registration, amplitude and phase correction, and height-direction focusing, the point clouds in the near area and the far area are obtained. The strongest points in the two sets of point clouds are iteratively selected to estimate and eliminate the defocused point clouds in the other set of point clouds until all the ambiguity is eliminated. Simulation experiments based on airborne measured data verified the capability to achieve HRWS 3D blur suppression of this method. Full article

This study proposes a novel technique for detecting aerial moving targets using multiple satellite radars. The approach enhances the image contrast of fused local three-dimensional (3D) profiles. Exploiting global navigation satellite system (GNSS) satellites as illuminators of opportunity (IOs) has brought remarkable innovations to multistatic radar. However, target detection is restricted by radiation sources since IOs are often uncontrollable. To address this, we utilize satellite radars operating in an active self-transmitting and self-receiving mode for controllability. The main challenge of multiradar target detection lies in effectively fusing the target echoes from individual radars, as the target ranges and Doppler histories differ. To this end, two periods, namely the integration period and detection period, are precisely designed. In the integration period, we propose a range difference-based positive and negative second-order Keystone transform (SOKT) method to make range compensation accurate. This method compensates for the range difference rather than the target range. In the detection period, we develop two weighting functions, i.e., the Doppler frequency rate (DFR) variance function and smooth spatial filtering function, to extract prominent areas and make efficient detection, respectively. Finally, the results from simulation datasets confirm the effectiveness of our proposed technique. Full article

As one of the promising technologies of 6G, dual-function radar communication (DFRC) integrates communication and radar sensing networks. However, with the application and deployment of DFRC, its security problem has become a significantly important issue. In this paper, we consider the physical layer security of a DFRC system where the base station communicates with multiple legitimate users and simultaneously detects the sensing target of interest. The sensing target is also a potential eavesdropper wiretapping the secure transmission. To this end, we proposed a secure design based on partial group successive interference cancellation through fully leveraging the split messages and partially decoding to improve the rate increment of legitimate users. In order to maximize the radar echo signal-to-noise ratio (SNR), we formulate an optimization problem of beamforming and consider introducing new variables and relaxing the problem to solve the non-convexity of the problem. Then, we propose a joint secure beamforming and rate optimization algorithm to solve the problem. Simulation results demonstrate the effectiveness of our design in improving the sensing and secrecy performance of the considered DFRC system. Full article

Aiming at the problem that ultrasonic detection is greatly affected by temperature drift, this paper investigates a novel temperature compensation algorithm. Ultrasonic impedance-based liquid-level measurement is a crucial non-contact, non-destructive technique. However, temperature drift can severely affect the accuracy of experimental measurements based on this technology. Theoretical analysis and experimental research on temperature drift phenomena are conducted in this study, accompanied by the proposal of a new compensation algorithm. Leveraging an external fixed-point liquid-level detection system experimental platform, the impact of temperature drift on ultrasonic echo energy and actual liquid-level height is examined. Experimental results demonstrate that temperature drift affects the speed and attenuation of ultrasonic waves, leading to decreased accuracy in measuring liquid levels. The proposed temperature compensation method yields an average relative error of 3.427%. The error range spans from 0.03 cm to 0.336 cm. The average relative error reduces by 21.535% compared with before compensation, showcasing its applicability across multiple temperature conditions and its significance in enhancing the accuracy of ultrasonic-based measurements. Full article

Deep-water reverberation caused by multiple reflections from the seafloor and sea surface can affect the performance of active sonars. To detect a moving target under reverberation conditions, a reverberation suppression method using multipath Doppler shift in deep water and wideband ambiguity function (WAF) is proposed. Firstly, the multipath Doppler factors in the deep-water direct zone are analyzed, and they are introduced into the target scattered sound field to obtain the echo of the moving target. The mesh method is used to simulate the deep-water reverberation waveform in time domain. Then, a simulation model for an active sonar based on the source and short vertical line array is established. Reverberation and target echo in the received signal can be separated in the Doppler shift domain of the WAF. The multipath Doppler shifts in the echo are used to estimate the multipath arrival angles, which can be used for target localization. The simulation model and the reverberation suppression detection method can provide theoretical support and a technical reference for the active detection of moving targets in deep water. Full article

As a special microwave detection system, multistatic radar has obvious advantages in covert operation, anti-jamming, and anti-stealth due to its configuration of spatial diversity. As a high-orbit irradiation source, a geosynchronous orbit satellite (GEO) has the advantages of a low revisit period, large beam coverage area, and stable power of ground beam compared with traditional passive radar irradiation sources. This paper focuses on the key technologies of flight target detection in multistatic radar based on geosynchronous orbit satellite irradiation with one transmitter and multiple receivers. We carry out the following work: Firstly, we aim to address the problems of low signal-to-noise ratio (SNR) and range cell migration of high-speed cruise targets. The Radon–Fourier transform constant false alarm rate detector-range cell migration correction (RFT-CFAR-RCMC) is adopted to realize the coherent integration of echoes with range cell migration correction (RCM) and Doppler phase compensation. It significantly improves the SNR. Furthermore, we utilize the staggered PRF to solve the ambiguity and obtain multi-view data. Secondly, based on the aforementioned target multi-view detection data, the linear least square (LLS) multistatic positioning method combining bistatic range positioning (BR) and time difference of arrival positioning (TDOA) is used, which constructs the BR and TDOA measurement equations and linearizes by mathematical transformation. The measurement equations are solved by the LLS method, and the target positioning and velocity inversion are realized by the fusion of multistatic data. Finally, using target positioning data as observation values of radar, the Kalman filter (KF) is used to achieve flight trajectory tracking. Numerical simulation verifies the effectiveness of the proposed process. Full article

The velocity/distance trade-off poses a fundamental challenge in pulsed Doppler weather radar systems and is known as the velocity/distance dilemma. Techniques such as multiple-pulse repetition frequency, staggered pulse repetition time (PRT), and pulse phase coding are commonly used to mitigate this issue. The current study evaluates the adaptability/capability of a specific type of low-capture signal called the non-uniform PRT (NPRT) through analyzing the weather target characteristics of typical velocity distributions. The spectral moments estimation (SME) signal-processing algorithm of the NPRT weather echo is designed to calculate the average power, velocity, and spectrum width of the target. A comprehensive error analysis is conducted to ascertain the efficacy of the NPRT processing algorithm under influencing factors. The results demonstrate that the spectral parameters of weather target echo with a velocity of $[-50,50]$ m/s through random-jitter NPRT signals align with radar functionality requirements (RFRs). Notably, the NPRT waveform resolves the inherent conflicts between the maximum unambiguous distance and velocity and elevates the upper limit of the maximal observation velocity. The evaluation results confirm that nonlinear radar signal processing technology can improve a radar’s detection performance and provide a new method for realizing the multifunctional observation of radar in different applications. Full article

To detect moving weak targets in the dual function radar communication (DFRC) system of an orthogonal frequency division multiplexing (OFDM) waveform, a modified hybrid integration method is addressed in this paper. A high-speed aircraft can cause range walk (RW) and Doppler walk (DW), rendering traditional detection methods ineffective. To overcome RW and DW, this paper proposes an integration approach combining DFRC and OFDM. The proposed approach consists of two primary components: intra-frame coherent integration and hybrid multi-inter-frame integration. After the echo signal is re-fragmented into multiple subfragments, the first step involves integrating energy across fixed situations within intra-frames for each subcarrier. Subsequently, coherent integration is performed across the subfragments, followed by the application of a Radon transform (RT) to generate frames based on the properties derived from the coherent integration output. This paper provides detailed expressions and analyses for various performance metrics of our proposed method, including the communication bit error ratio (BER), responses of coherent and non-coherent outputs, and probability of detection. Simulation results demonstrate the effectiveness of our strategy. Full article

Due to the similarity in wavelength between millimeter-wave (MMW) signals and raindrop diameters, rainfall induces significant attenuation and scattering effects that challenge the detection performance of MMW fuzes in rainy environments. To enhance the adaptability of frequency-modulated MMW fuzes in such conditions, the effects of rain on MMW signal attenuation and scattering are investigated. A mathematical model for the multipath echo signals of the fuze was developed. The Monte Carlo method was employed to simulate echo signals considering multiple scattering, and experimental validations were conducted. The results from simulations and experiments revealed that rainfall increases the bottom noise of the echo signal, with rain backscatter noise predominantly affecting the lower end of the echo signal spectrum. However, rain conditions below torrential levels did not significantly impact the detection of strong reflection targets at the high end of the spectrum. The modeling approach and findings presented offer theoretical support for designing MMW fuzes with improved environmental adaptability. Full article

The Doppler-spread problem is commonly encountered in space target observation scenarios using ground-based radar when prolonged coherent integration techniques are utilized. Even when the translational motion is accurately compensated, the phase resulting from changes in the target observation attitude (TOA) still leads to extension of the target’s echo energy across multiple Doppler cells. In particular, as the TOA change undergoes multiple cycles within a coherent processing interval (CPI), the Doppler spectrum spreads into equidistant sparse line spectra, posing a substantial challenge for target detection. Aiming to address such problems, we propose a generalized likelihood ratio test based on overlapping group shrinkage denoising and order statistics (OGSos-GLRT) in this study. First, the Doppler domain signal is denoised according to its equidistant sparse characteristics, allowing for the recovery of Doppler cells where line spectra may be situated. Then, several of the largest Doppler cells are integrated into the GLRT for detection. An analytical expression for the false alarm probability of the proposed detector is also derived. Additionally, a modified OGSos-GLRT method is proposed to make decisions based on an increasing estimated number of line spectra (ENLS), thus increasing the robustness of OGSos-GLRT when the ENLS mismatches the actual value. Finally, Monte Carlo simulations confirm the effectiveness of the proposed detector, even at low signal-to-noise ratios (SNRs). Full article

In the fields of agriculture and forestry, the Normalized Difference Vegetation Index (NDVI) is a critical indicator for assessing the physiological state of plants. Traditional imaging sensors can only collect two-dimensional vegetation distribution data, while dual-wavelength LiDAR technology offers the capability to capture vertical distribution information, which is essential for forest structure recovery and precision agriculture management. However, existing LiDAR systems face challenges in detecting echoes at two wavelengths, typically relying on multiple detectors or array sensors, leading to high costs, bulky systems, and slow detection rates. This study introduces a time-stretched method to separate two laser wavelengths in the time dimension, enabling a more cost-effective and efficient dual-spectral (600 nm and 800 nm) LiDAR system. Utilizing a supercontinuum laser and a single-pixel detector, the system incorporates specifically designed time-stretched transmission optics, enhancing the efficiency of NDVI data collection. We validated the ranging performance of the system, achieving an accuracy of approximately 3 mm by collecting data with a high sampling rate oscilloscope. Furthermore, by detecting branches, soil, and leaves in various health conditions, we evaluated the system’s performance. The dual-wavelength LiDAR can detect variations in NDVI due to differences in chlorophyll concentration and water content. Additionally, we used the radar equation to analyze the actual scene, clarifying the impact of the incidence angle on reflectance and NDVI. Scanning the Red Sumach, we obtained its NDVI distribution, demonstrating its physical characteristics. In conclusion, the proposed dual-wavelength LiDAR based on the time-stretched method has proven effective in agricultural and forestry applications, offering a new technological approach for future precision agriculture and forest management. Full article

In neuroimaging, there is no equivalent alternative to magnetic resonance imaging (MRI). However, image acquisitions are generally time-consuming, which may limit utilization in some cases, e.g., in patients who cannot remain motionless for long or suffer from claustrophobia, or in the event of extensive waiting times. For multiple sclerosis (MS) patients, MRI plays a major role in drug therapy decision-making. The purpose of this study was to evaluate whether an ultrafast, T2-weighted (T2w), deep learning-enhanced (DL), echo-planar-imaging-based (EPI) fluid-attenuated inversion recovery (FLAIR) sequence (FLAIR_UF) that has targeted neurological emergencies so far might even be an option to detect MS lesions of the brain compared to conventional FLAIR sequences. Therefore, 17 MS patients were enrolled prospectively in this exploratory study. Standard MRI protocols and ultrafast acquisitions were conducted at 3 tesla (T), including three-dimensional (3D)-FLAIR, turbo/fast spin-echo (TSE)-FLAIR, and FLAIR_UF. Inflammatory lesions were grouped by size and location. Lesion conspicuity and image quality were rated on an ordinal five-point Likert scale, and lesion detection rates were calculated. Statistical analyses were performed to compare results. Altogether, 568 different lesions were found. Data indicated no significant differences in lesion detection (sensitivity and positive predictive value [PPV]) between FLAIR_UF and axially reconstructed 3D-FLAIR (lesion size ≥3 mm × ≥2 mm) and no differences in sensitivity between FLAIR_UF and TSE-FLAIR (lesion size ≥3 mm total). Lesion conspicuity in FLAIR_UF was similar in all brain regions except for superior conspicuity in the occipital lobe and inferior conspicuity in the central brain regions. Further findings include location-dependent limitations of signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) as well as artifacts such as spatial distortions in FLAIR_UF. In conclusion, FLAIR_UF could potentially be an expedient alternative to conventional methods for brain imaging in MS patients since the acquisition can be performed in a fraction of time while maintaining good image quality. Full article

In the environment of smoke and suspended particles, the accurate detection of targets is one of the difficulties for frequency-modulated continuous-wave (FMCW) laser fuzes to work properly in harsh conditions. To weaken and eliminate the significant influence caused by the interaction of different systems in the photon transmission process and the smoke particle environment, it is necessary to increase the amplitude of the target echo signal to improve the signal-to-noise ratio (SNR), which contributes to enhancing the detection performance of the laser fuze for the ground target in the smoke. Under these conditions, the particle transmission of photons in the smoke environment is studied from the perspective of three-dimentional (3D) collisions between photons and smoke particles, and the modeling and Unity3D simulation of FMCW laser echo signal based on 3D particle collision is conducted. On this basis, a laser fuze structure based on multiple channel beam emission is designed for the combined effect of particle features from different systems and its impact on the target characteristics is researched. Simulation results show that the multiple channel laser emission enhances the laser target echo signal amplitude and also improves the anti-interference ability against the combined effects of multiple particle features compared with the single channel. Through the validation based on the laser prototype with four-channel beam emitting, the above conclusions are supported by the experimental results. Therefore, this study not only reveals the laser target properties under the 3D particle collision perspective, but also reflects the reasonableness and effectiveness of utilizing the target characteristics in the 3D particle collision mode to enhance the detection performance of FMCW laser fuze in the smoke. Full article