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Aiming at the large-scale application of metasurface in the field of radar stealth, we present a hybrid resonance-based and dispersion substrate integrated checkerboard metasurface (CMS) for reducing the radar cross section (RCS) of flat metallic targets. Considering the frequency-dependent characteristics of such a dispersion material, a pair of single and dual resonant artificial magnetic conductor meta-atoms with the modified "crusades-like" cell topologies is employed to maximize the operating bandwidth; besides, a comprehensive and thorough investigation on the resonance mechanism is conducted in this paper to provide an intuitive physical insight of meta-atoms' reflection responses. By comparing the predicted results with simulations, the quasi-periodic effect is introduced to explain the frequency shift of 10 dB RCS reduction bandwidth. In the implementation procedure, a prototype of the designed RCS reducer with a total dimension of 180 × 180 mm2 is fabricated and measured, the 10 dB RCS reduction bandwidth of theoretical simulation and experimental measurement are basically consistent, and the performance improvement of 8 dB RCS reduction in the experimental results can be attributed to the dispersion effects of the dielectric substrate. With a better figure of merit, our efforts may serve as a useful exemplar for the economical CMS architecture in radar evasive applications. [ABSTRACT FROM AUTHOR]

Accurate and precise KDP estimates are essential for radar-based applications, especially in quantitative precipitation estimation and radar data quality control routines. The accuracy of these estimates largely depends on the post-processing of the radar's measured Φ DP , which aims to reduce noise and backscattering effects while preserving fine-scale precipitation features. In this study, we evaluate the performance of several publicly available KDP estimation methods implemented in open-source libraries such as PyArt and Wradlib, and the method used in the Vaisala weather radars. To benchmark these methods, we employ a polarimetric self-consistency approach that relates KDP to reflectivity and differential reflectivity in rain, providing a reference self-consistency KDP (K DPSC) for comparison. This approach allows for the construction of the reference KDP observations that can be used to assess the accuracy and robustness of the studied KDP estimation methods. We assess each method by quantifying uncertainties using C-band weather radar observations where the reflectivity values ranged between 20 and 50 dBZ. Using the proposed evaluation framework we could define optimized parameter settings for the methods that have user-configurable parameters. Most of such methods showed significant reduction in the estimation errors after the optimization with respect to the default settings. We have found significant differences in the performances of the studied methods, where the best performing methods showed smaller normalized biases in the high reflectivity values (i.e., ≥ 40 dBZ) and overall smaller normalized root mean squared errors across the range of reflectivity values. [ABSTRACT FROM AUTHOR]

Sea state bias (SSB) is a crucial error of satellite radar altimetry over the ocean surface. For operational nonparametric SSB (NPSSB) models, such as two-dimensional (2D) or three-dimensional (3D) NPSSB, the solution process becomes increasingly complex and the construction of their regression functions pose challenges as the dimensionality of relevant variables increases. And most current SSB correction models for altimeters still follow those of traditional nadir radar altimeters, which limits their applicability to Synthetic Aperture Radar altimeters. Therefore, to improve this situation, this study has explored the influence of multi-dimensional SSB models on Synthetic Aperture Radar altimeters. This paper proposes a deep learning-based SSB estimation model called SNSSB, which employs a Siamese network framework, takes various multi-dimensional variables related to sea state as inputs, and uses the difference in sea surface height (SSH) at self-crossover points as the label. Experiments were conducted using Sentinel-6 self-crossover data from 2021 to 2023, and the model is evaluated using three main metrics: the variance of the SSH difference, the explained variance, and the SSH difference variance index (SVDI). The experimental results demonstrate that the proposed SNSSB model can further improve the accuracy of SSB estimation. On a global scale, compared to the traditional NPSSB, the multi-dimensional SNSSB not only decreases the variance of the SSH difference by over 11%, but also improves the explained variance by 5-10 cm2 in mid- and low-latitude regions. And the regional SNSSB also performs well, reducing the variance of the SSH difference by over 10% compared to the NPSSB. Additionally, the SNSSB model improves the computational efficiency by approximately 100 times. The favorable results highlight the potential of the multidimensional SNSSB in constructing SSB models, particularly the five-dimensional (5D) SNSSB, representing a breakthrough in overcoming the limitations of traditional NPSSB for constructing high-dimensional models. This study provides a novel approach to exploring the multiple influencing factors of SSB. [ABSTRACT FROM AUTHOR]

This study examines the differences related to microphysical properties of ice in thunderstorms over the Amazon and Congo Basin using the Precipitation Feature (PF) data sets derived from passive microwave and radar observations from the Tropical Rainfall Measuring Mission and Global Precipitation Mission Core Satellites. Analysis reveals that Amazon thunderstorms are likely composed of ice crystals smaller but more numerous than those in the Congo Basin, resulting in half as many flashes per PF on average in the Amazon, for similar Ice Water Content (IWC) or Area of 30 dBZ at −10°C (Acharge). The increase of the flash count following an increase of the IWC (Acharge) is only 72% (61%) as effective in the Amazon as it would be in the Congo Basin area. PFs with similar 30 dBZ radar echo top heights exhibit lower Brightness Temperatures (TBs) in the 85/89, 165, and 183 GHz frequencies over the Amazon, indicating more numerous smaller ice particles compared to those over the Congo Basin, which tend to show colder TBs at 37 GHz, possibly due to more numerous large graupel or hail particles. Comparisons of TBs in PFs with similar 30 dBZ echo top temperature between the Amazon and 3 × 3º global grids show that the median TB in Amazon is higher than that in most oceanic areas but is comparable to areas having high oceanic lightning activity (e.g., South Pacific Convergence Zone). It suggests that systems in the Amazon have similarities with maritime precipitation systems, yet with distinct characteristics indicative of land systems. Plain Language Summary: A comparison is made between Amazon (AM) and Congo Basin (CB) thunderstorms with similar reflectivity values seen by either TRMM or GPM, versus other variables that can help us understand the ice microphysics of these storms (IWC, area of 30 dBZ at −10C, Flash count, TBs). The radar reflectivity being driven by number and size (to the sixth power) of hydrometeors, the hypothesis is that for two storms with a same reflectivity value, one in AM and one in CB, the reflectivity value measured is driven by a higher concentration of large ice particles in CB (i.e., driven by size), while it is driven by a higher concentration of smaller ice particles in AM (i.e., driven by number). We quantify the lightning count difference between the two areas as a function of the amount of ice in the thunderstorm. It shows that AM produces significantly less lightning than CB on average for a similar ice content. Radiometers from TRMM and GPM are then used to compare the median TBs observed in the Amazon with the rest of the globe, to emphasize once again that AM is producing systems that are neither of land nor oceanic nature, but somewhere in the middle. Key Points: Amazonian systems are principally composed of smaller and more numerous ice crystalsIncreasing the Ice Water Content (IWC) and/or core size of thunderstorms in the Amazon demonstrates lower effectiveness in boosting lightning activityAmazonian systems present brightness temperatures unlike land or oceans but similar to oceanic regions with greater lightning activity [ABSTRACT FROM AUTHOR]

Human falls occur rarely; however, they can lead to severe consequences if not addressed immediately. With the rise of nuclear families, there has been a significant increase in the risk of unnoticed falls leading to even death. Therefore, an efficient fall monitoring system is essential for prompt assistance and minimizing fall risks. Several strategies have been proposed with technological advancements, and some devices are already available on the market. Among the proposed systems, radar technology has emerged as a state-of-the-art noninvasive technique due to its attractive and unique features, such as the ability to operate under occlusion, dirt, and fog without compromising the person's privacy. This paper presents a systematic review of a state-of-the-art fall detection system based on radar technology. While various reviews on fall-related technologies exist, none specifically focus on radar-based systems. To our knowledge, we are the first to provide a review in such detail of state-of-the-art radar sensor-based fall detection. We comprehensively outline the radar-based fall monitoring system and the corresponding data processing techniques. Finally, we conclude our paper by discussing current trends, future research direction and challenges in this research field. This paper aims to help researchers in the field of noninvasive fall detection and facilitate future research. [ABSTRACT FROM AUTHOR]

Because of their high sensitivity to hydrometeors and their high vertical resolutions, space-borne radar observations are emerging as an undeniable asset for Numerical Weather Prediction (NWP) applications. The EUMETSAT (European Organisation for the Exploitation of Meteorological Satellites) NWP SAF (Satellite Application Facility) released an active sensor module within version 13 of the RTTOV (Radiative Transfer for TOVS) software with the goal of simulating both active and passive microwave instruments within a single framework using the same radiative transfer assumptions. This study provides an in-depth description of the radar simulator available within this software. In addition, this study proposes a revised version of the existing melting layer parametrization scheme of Bauer (2001) within the RTTOV-SCATT v13.1 model to provide a better fit to observations below the freezing level. Simulations are performed with and without melting layer schemes for the Dual precipitation radar (DPR) instrument onboard GPM using the ARPEGE (Action de Recherche Petite Echelle Grande Echelle) global NWP model running operationally at Météo-France for two different one-month periods (June, 2020 and January, 2021). Results for a case study over the Atlantic ocean show that the revised melting scheme produces more realistic simulations as compared to the default scheme both at Ku (13.5 GHz) and Ka (35.5 GHz) frequencies and these simulations are much closer to observations. A statistical assessment using more samples show significant improvement of the first-guess departure statistics with the revised scheme compared to the existing melting scheme. As a step further, this study showcases the use of melting layer simulations for the classification of precipitation (stratiform, convective and transition) using the Dual Frequency Ratio algorithm (DFR). The classification results also reveal a significant overestimation of the rain reflectivities in all hemispheres, which can either be due to a tendency of the ARPEGE model to produce a too large amount of convective precipitation, or to a mis-representation of the convective precipitation fraction within the forward operator. [ABSTRACT FROM AUTHOR]

This article introduces a novel microstrip slot antenna array (SAA) configuration for radar applications. The proposed antenna is specifically designed for operation in the K-band, spanning from 23 to 24.3 GHz. The antenna structure comprises two substrates: the feed network and ground plane are on the bottom substrate, and the radiating slots are on the top layer of the first substrate. The incorporation of a unique grid feed configuration, featuring 50 Ohm center excitation for the first time, improves the feed mechanism of the microstrip SAA. This innovation contributes to achieving a compact size and high gain. To enhance the side lobe level, the design incorporates a substrate-integrated waveguide-backed cavity, which significantly reduces surface waves. The SAA consists of 25 radiating elements with a gain of 14 dBi. In the elevation and azimuth planes, the half-power beamwidths are measured at 12.1° and 69.1°, respectively. The proposed antenna array's measured impedance bandwidth ranges from 23.15 to 24.75 GHz, guaranteeing a reflection coefficient (S11) of less than − 10 dB. The suggested antenna's applicability for automotive multi-input multi-output radar has been validated. [ABSTRACT FROM AUTHOR]

The Earth Cloud, Aerosol and Radiation Explorer (EarthCARE) satellite developed by the European Space Agency (ESA) and the Japan Aerospace Exploration Agency (JAXA) launched in May 2024 carries a novel 94-GHz Cloud Profiling Radar (CPR) with Doppler capability. This work describes the open-source instrument simulator Orbital-Radar, which transforms high-resolution radar data from field observations or forward simulations of numerical models to CPR primary measurements and uncertainties. The transformation accounts for sampling geometry and surface effects. We demonstrate Orbital-Radar's ability to provide realistic CPR views of typical cloud and precipitation scenes. These results provide valuable insights into the capabilities and challenges of the EarthCARE CPR mission and its advantages over the CloudSat CPR. Finally, Orbital-Radar allows for the evaluation of kilometer-scale numerical weather prediction models with EarthCARE CPR observations. [ABSTRACT FROM AUTHOR]

The ability of a radar to discriminate in both range and Doppler velocity is completely characterized by the ambiguity function (AF) of its transmit waveform. Mathematically, it is obtained by correlating the waveform with its Doppler-shifted and delayed replicas. We consider the inverse problem of designing a radar transmit waveform that satisfies the specified AF magnitude. This process may be viewed as a signal reconstruction with some variation of phase retrieval methods. We provide a trust-region algorithm that minimizes a smoothed non-convex least-squares objective function to iteratively recover the underlying signal-of-interest for either time- or band-limited support. The method first approximates the signal using an iterative spectral algorithm and then refines the attained initialization based on a sequence of gradient iterations. Our theoretical analysis shows that unique signal reconstruction is possible using signal samples no more than thrice the number of signal frequencies or time samples. Numerical experiments demonstrate that our method recovers both time- and band-limited signals from sparsely and randomly sampled, noisy and noiseless AFs. [ABSTRACT FROM AUTHOR]

This paper proposes a novel drone detection method based on a convolutional neural network (CNN) utilizing range–Doppler map images from a frequency-modulated continuous-wave (FMCW) radar. The existing drone detection and identification techniques, which rely on the micro-Doppler signature (MDS), face challenges when a drone is small or located far away, leading to performance degradation due to signal attenuation and faint (MDS). In order to address these issues, this paper suggests a method where multiple time-series range–Doppler images from an FMCW radar are overlaid onto a single image and fed to a CNN. The experimental results, using actual data for three different drone sizes, show significant performance improvements in drone detection accuracy compared to conventional methods. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The orthogonal frequency-division multiplexing (OFDM) mode with a linear frequency modulation (LFM) signal as the baseband waveform has been widely studied and applied in multiple-input multiple-output (MIMO) radar systems. However, its high sidelobe levels after pulse compression affect the target detection of radar systems. For this paper, theoretical analysis was performed, to investigate the causes of high sidelobe levels in OFDM-LFM waveforms, and a novel waveform optimization design method based on deep neural networks is proposed. This method utilizes the classic ResNeXt network to construct dual-channel neural networks, and a new loss function is employed to design the phase and bandwidth of the OFDM-LFM waveforms. Meanwhile, the optimization factor is exploited, to address the optimization problem of the peak sidelobe levels (PSLs) and integral sidelobe levels (ISLs). Our numerical results verified the correctness of the theoretical analysis and the effectiveness of the proposed method. The designed OFDM-LFM waveforms exhibited outstanding performance in pulse compression and improved the detection performance of the radar. [ABSTRACT FROM AUTHOR]

Synthetic aperture radar (SAR) simulation is a vital tool for planning SAR missions, interpreting SAR images, and extracting valuable information. SAR imaging is essential for analyzing sea scenes, and the accuracy of sea surface and scattering models is crucial for effective SAR simulations. Traditional methods typically employ empirical formulas to fit sea surface scattering, which are not closely aligned with the principles of electromagnetic scattering. This paper introduces a novel approach by constructing multiple sea surface models based on the Pierson–Moskowitz (P-M) sea spectrum, integrated with the stereo wave observation projection (SWOP) expansion function to thoroughly account for the influence of wave fluctuation characteristics on radar scattering. Utilizing the shooting and bouncing ray-physical optics (SBR-PO) method, which adheres to the principles of electromagnetic scattering, this study not only analyzes sea surface scattering characteristics under various sea conditions but also facilitates the computation of scattering coupling between multiple targets. By constructing detailed scattering distribution data, the method achieves high-precision SAR simulation results. The scattering model developed using the SBR-PO method provides a more nuanced description of sea surface scenes compared to traditional methods, achieving an optimal balance between efficiency and accuracy, thus significantly enhancing sea surface SAR imaging simulations. [ABSTRACT FROM AUTHOR]

Entomological radar is one of the most effective tools for monitoring insect migration, capable of detecting migratory insects concentrated in layers and facilitating the analysis of insect migration behavior. However, traditional entomological radar, with its low resolution, can only provide a rough observation of layer concentrations. The advent of High-Resolution Phased Array Radar (HPAR) has transformed this situation. With its high range resolution and high data update rate, HPAR can generate detailed concentration spatiotemporal distribution heatmaps. This technology facilitates the detection of changes in insect concentrations across different time periods and altitudes, thereby enabling the observation of large-scale take-off, landing, and layering phenomena. However, the lack of effective techniques for extracting insect concentration data of different phenomena from these heatmaps significantly limits detailed analyses of insect migration patterns. This paper is the first to apply instance segmentation technology to the extraction of insect data, proposing a method for segmenting and extracting insect concentration data from spatiotemporal distribution heatmaps at different phenomena. To address the characteristics of concentrations in spatiotemporal distributions, we developed the Heatmap Feature Fusion Network (HFF-Net). In HFF-Net, we incorporate the Global Context (GC) module to enhance feature extraction of concentration distributions, utilize the Atrous Spatial Pyramid Pooling with Depthwise Separable Convolution (SASPP) module to extend the receptive field for understanding various spatiotemporal distributions of concentrations, and refine segmentation masks with the Deformable Convolution Mask Fusion (DCMF) module to enhance segmentation detail. Experimental results show that our proposed network can effectively segment concentrations of different phenomena from heatmaps, providing technical support for detailed and systematic studies of insect migration behavior. [ABSTRACT FROM AUTHOR]

Interferometric radars are widely used sensors for structural health monitoring. They are able to perform dynamic measurements of displacement with sub-millimeter precision. Today, the Ku-band is the most common, due to the spread of commercial systems operating in this band. At the same time, the W-band sensors are gaining ever more interest. Other popular systems work in the X-band. Since the characteristics of the measurements dramatically depend on the operative frequency, it is essential to highlight their differences. For instance, higher frequency allows for high displacement resolution, but it is more subject to phase wrapping and decorrelation effects. In this paper, a direct comparison between radars operating in X, Ku, and W-band for bridge monitoring is carried out. The radars provide frequency-modulated continuous-wave signals. Experimental campaigns were performed both in controlled and realistic scenarios (a stayed bridge). The results of the experiments demonstrate that all the three sensors are suitable for performing dynamic structure monitoring despite their differences. It is worth noting that this comparative analysis has highlighted the role of amplitude variation in phase/displacement measurement. Regarding this point, the three different bands exhibit significant differences. [ABSTRACT FROM AUTHOR]

The High-Frequency (HF) radar network has become an effective method for detecting coastal currents. In this study, we confirmed the effectiveness of the HF radar measurements by comparing with the Acoustic Doppler Current Profiler (ADCP) and explore the possibility of assimilating radar data into a regional coastal ocean model. A regional high-resolution model with resolution of 10 m was first built in the Pearl River Estuary (PRE). However, analysis of the Hovmöller diagrams from the model simulations in this study indicated a significant deficiency in representing Near-Inertial Oscillations (NIOs) in the PRE, particularly in the east–west direction, despite including wind fields in the input data, during the week from 3 to 8 August 2022. To overcome the model deficiency, we conducted a set of assimilation experiments and performed sensitivity analyses. The results of sensitivity experiments indicate that the model exhibits a sufficient capacity to replicate NIOs after assimilation, lasting approximately 5–6 days. To further analyze the reasons for the decay in the magnitude of the NIOs, data from the three ADCP stations were compared with model results by applying the momentum equation. The assimilated vertical diffusion term outperforms the unassimilated model in representing NIOs. These findings highlight the importance of the vertical diffusion term for simulating NIOs and the data assimilation in improving the model's representation of physical processes. [ABSTRACT FROM AUTHOR]

In developing modern intelligent transportation systems, integrated sensing and communication (ISAC) technology has become an efficient and promising method for vehicle road services. To enhance traffic safety and efficiency through real-time interaction between vehicles and roads, this paper proposes a searching–deciding scheme for an alternation radar-communication (radar-comm) system. Firstly, its communication performance is derived for a given detection probability. Then, we process the echo data from real-world millimeter-wave (mmWave) radar into four-dimensional (4D) point cloud datasets and thus separate different hybrid modes of single-vehicle and vehicle fleets into three types of scenes. Based on these datasets, an efficient labeling method is proposed to assist accurate vehicle target detection. Finally, a novel vehicle detection scheme is proposed to classify various scenes and accurately detect vehicle targets based on deep learning methods. Extensive experiments on collected real-world datasets demonstrate that compared to benchmarks, the proposed scheme obtains substantial radar performance and achieves competitive communication performance. [ABSTRACT FROM AUTHOR]

With the advancement of radar technology toward multifunctionality and cognitive capabilities, traditional radar countermeasures are no longer sufficient to meet the demands of countering the advanced multifunctional radar (MFR) systems. Rapid and accurate generation of the optimal jamming strategy is one of the key technologies for efficiently completing radar countermeasures. To enhance the efficiency and accuracy of jamming policy generation, an efficient jamming policy generation method based on multi-timescale ensemble Q-learning (MTEQL) is proposed in this paper. First, the task of generating jamming strategies is framed as a Markov decision process (MDP) by constructing a countermeasure scenario between the jammer and radar, while analyzing the principle radar operation mode transitions. Then, multiple structure-dependent Markov environments are created based on the real-world adversarial interactions between jammers and radars. Q-learning algorithms are executed concurrently in these environments, and their results are merged through an adaptive weighting mechanism that utilizes the Jensen–Shannon divergence (JSD). Ultimately, a low-complexity and near-optimal jamming policy is derived. Simulation results indicate that the proposed method has superior jamming policy generation performance compared with the Q-learning algorithm, in terms of the short jamming decision-making time and low average strategy error rate. [ABSTRACT FROM AUTHOR]

The transmit/receive-receive (T/R-R) synergetic High Frequency Surface Wave Radar (HFSWR) has increasingly attracted attention due to its high localization accuracy, but multi-target pairing needs to be performed before localization in multi-target scenarios. However, existing multi-target parameter matching methods have primarily focused on track association, which falls under the category of information-level fusion techniques, with few methods based on detected points. In this paper, we propose a multi-target pairing method with high computational efficiency based on angle information for T/R-R synergetic HFSWR. To be more specific, a dual-receiving array signal model under long baseline condition is firstly constructed. Then, the amplitude and phase differences of the same target reaching two subarrays are calculated to establish the cross-correlation matrix. Subsequently, in order to extract the rotation factor matrices containing pairing information and improve angle estimation performance, we utilize the conjugate symmetry properties of the uniform linear array (ULA) manifold matrix for generalized virtual aperture extension. Ultimately, azimuths estimation and multi-target pairing are accomplished by combining the propagator method (PM) and the ESPRIT algorithm. The proposed method relies solely on angle information for multi-target pairing and leverages the rotational invariance property of Vandermonde matrices to avoid peak searching or iterations, making it computationally efficient. Furthermore, the proposed method maintains superb performance regardless of whether the spatial angles are widely separated or very close. Simulation results validate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The onset, evolution, and propagation processes of convective cells can be reflected by the organizational morphology of mesoscale convective systems (MCSs), which are key factors in determining the potential for heavy precipitation. This paper proposed a method for objectively classifying and segmenting MCSs using geosynchronous satellite observations. Validation of the product relative to the classification in radar composite reflectivity imagery indicates that the algorithm offers skill for discriminating between convective and stratiform areas and matched 65% of convective area identifications in radar imagery with a false alarm rate of 39% and an accuracy of 94%. A quantitative evaluation of the similarity between the structures of 50 MCSs randomly obtained from satellite and radar observations shows that the similarity was as high as 60%. For further testing, the organizational modes of the MCS that caused the heavy precipitation in Northwest China on August 21, 2016 (hereinafter known as the "0821" rainstorm) were identified. It was found that the MCS, accompanied by the "0821" rainstorm, successively exhibited modes of the isolated cell, squall line with parallel stratiform (PS) rain, and non-linear system during its life cycle. Among them, the PS mode might have played a key role in causing this flooding. These findings are in line with previous studies. [ABSTRACT FROM AUTHOR]

Ship response in short-crested irregular waves is calculated as the product of the frequency response function of ship response in regular waves and the directional spectrum of waves. However, the commonly used IACS and JONSWAP spectra may not accurately reproduce detailed spectra by wave hindcasting data and wave radar measurements, leading to over- or underestimation of ship responses. This study focuses on the reproducibility of a detailed directional spectrum by standard directional spectrum based on physical considerations concerning the mean spreading angle. This approach facilitates discussion on the limitations of reproducing a detailed directional spectrum with a standard directional spectrum. Furthermore, a procedure is proposed for the use of a standard directional spectrum based on the mean spreading angle as an indicator. Analysis using 6 example ships demonstrates that employing the procedure enables the extraction of highly reproducible and valuable data for short-term prediction of the added resistance in short-crested irregular waves. [ABSTRACT FROM AUTHOR]

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This article develops vehicle type–based crash-prediction models for cars and heavy commercial vehicles (HCVs) as a function of the curve geometry and vehicle-based design consistency criteria under heterogeneous traffic conditions on two-lane, two-way rural highways, specifically in hilly terrains. A National Highway (NH-953) connecting Netrang and Rajpipla in India was selected. There are 38 curves in the study section, each having a different curve geometry. Speed data were collected using the radar gun for cars and HCVs. The geometric design consistency was evaluated using Criterion I (the difference between operating and design speeds). The results show that 53% of the curves for cars have good consistency, compared to 32% and 29% of the curves for HCVs, which have fair and poor consistency, respectively. The Poisson-Tweedie regression technique, which provides a unified framework to model over-dispersed, under-dispersed, zero-inflated, count-data, and multiple-response variables, was used to develop the crash prediction models. The results revealed that crashes (cars and HCVs) decrease as the curve radius, deflection angle, and length increase. Similarly, as the tangent length increases, the difference between operating and design speeds increases, making inconsistent highway alignment, resulting in increased chances of crashes. The results of the present study can help highway authorities to evaluate highway alignment consistency and develop corresponding proactive strategies to improve highway safety. [ABSTRACT FROM AUTHOR]

Regarding the suppression of radar mainlobe jamming, a blind extraction method for suppressing mainlobe jamming based on maximizing the signal to interference plus noise ratio (SINR) is proposed. Unlike blind separation, blind extraction can extract interested components from multiple mixed signals, making it more suitable for jamming suppression in complex electromagnetic environments with multiple sources and channels. This method establishes an optimization model for maximizing SINR in the distance domain. The target echo signal is solved and extracted using the particle swarm optimization (PSO) algorithm to achieve mainlobe jamming suppression. Simulation testing shows that the proposed method improves the jamming suppression effect compared to traditional blind separation jamming suppression methods. It does not require estimation of the number of sources and has lower requirements for the number of channels, making it applicable in underdetermined scenarios. Additionally, it reduces computational complexity, making it more suitable for complex electromagnetic environments. [ABSTRACT FROM AUTHOR]

To solve the problem on low classification accuracy caused by the difficulty of extracting features of micro-motion targets, such as pedestrians, vehicles, and drones in low-altitude radar monitoring scenarios, a multi-channel radar target micro-motion feature classification method based on EfficientNet is proposed. Firstly, a multi-energy singular value decomposition method is proposed to suppress clutter and noise, and enhance the micro-motion characteristics of the target based on the energy distribution differences between the clutter, target, and noise signals. Secondly, the multi-channel EfficientNet model is designed to combine time-frequency information features in radar sum and difference channels, and further fuse multi-channel micro-motion features to achieve accurate target classification. Finally, the effectiveness of the proposed method is visited through experiments using radar measured target data. The results show that compared with other methods, the proposed method significantly improves classification accuracy with low model complexly. [ABSTRACT FROM AUTHOR]

To address the technical problems of target detection and identification of missile-borne radar in the background of strong ground clutter, we propose to combine the high resolution range profile (HRRP) technology and Doppler beam sharpening (DBS) technology to carry out two-dimensional high-resolution imaging of the ground to improve the performance of radar target detection and identification in the presence of clutter. The method uses the linear frequency modulation stepped frequency (LFM-SF) signal as the basic waveform. Firstly, the Doppler effect caused by the platform speed is discussed in detail and corrected. Then, HRRP sequences corresponding to each frame are obtained through distance profile extraction, and aiimuth high resolution is achieved using fast Fourier transform (FFT). Finally, motion compensation is applied to the errors caused by the platform in actual light conditions, completing the two-dimensional resolution of the area within the beam. The processing of measured data has verified the effectiveness and practicality of the proposed algorithm. [ABSTRACT FROM AUTHOR]

The intermittent sampling repeater jamming (ISRJ) technology is a clever coherent multi-false target jamming technology. New research on ISRJ against different radar processing systems and different radar waveforms has become one of the hotspots in the current radar countermeasures field. In order to further clarify the characteristics of ISRJ and corresponding expanded application prospects, the background and basic principles of ISRJ has been reviewed firstly. Then, the detailed development history of ISRJ has also been outlined and the new ISRJ types were summarized and classified. At last, the expanded application of intermittent sampling in radar detection and other fields was introduced, aiming to provide some reference for experts and scholars. [ABSTRACT FROM AUTHOR]

Focusing on the issue of error detection in terminal guidance radar under sea clutter background, research on sea clutter suppression and discrimination is conducted. The target identification and classification algorithm based on Tri-feature training is adopted, and the target amplitude, peak duration range and fluctuation rate are used as feature quantities. The radial basis function (RBF) is selected as the kernel function, which has strong nonlinear mapping ability and can better represent the relationship between data in high-dimensional space. Then, the support vector machine ( SVM) target identification and classification classifier design and experimental data verification are carried out. Verified by publicly available measured data, the accuracy rate of the proposed algorithm has reached over 97%. Compared with the traditional template matching recognition method, the target identification and classification algorithm based on Tri-feature training has higher identification accuracy, which proves the effectiveness and progressiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Purpose of Review: The radiologist's role is integral in the diagnosis and preoperative management of breast cancer patients. The pre-surgical localization of non-palpable breast masses has been achieved using wire-guided devices for decades. Despite being widely regarded as the gold standard for their proven effectiveness and cost-effectiveness, wire-guided localization (WGL) techniques have been associated with several drawbacks. These drawbacks include patient discomfort, the possibility of displacement prior to resection, and the requirement for placement on the same day as surgery. This review aims to outline non-wire localization (NWL) devices and techniques available as alternatives to WGL, including their benefits and limitations. Recent Findings: No significant differences have been found with surgical outcomes between WGL and NWL. Between 2013 and 2018, the use of WGL has decreased from 75 to 32%, and the use of radioactive seed localization has increased from 16 to 61%. Summary: A multidisciplinary approach that considers the patient, provider, and healthcare institution and current research on surgical outcomes must be utilized to determine the optimal course of action for preoperative localization of non-palpable breast masses. [ABSTRACT FROM AUTHOR]

Policymakers demonstrate a keen interest in understanding risky driving behaviors to formulate effective countermeasures aimed at reducing accidents and economic losses. With the increasing deployment of millimeter-wave (MMW) radars on roadways, there exists a viable opportunity to gather extensive vehicle information at big data levels from individual drivers traversing through the radar detection range. This study endeavors to analyze traffic flow characteristics and identify risky driving behaviors using the noisy raw vehicle position and speed profiles obtained from MMW radars installed on a highway in China. A series of data cleaning procedures are meticulously implemented to address several typical trajectory errors stemming from MMW radars. Subsequently, after data cleaning, the study identifies risky driving behaviors through established methods found in the literature and evaluates the prevalence of these behaviors across different times of day and days of the week. This research mitigates the gap between raw vehicle trajectories from MMW radar and popular existing risk analysis methods. In addition, this research analyzes the temporal pattern of different risks and pinpoints their inherent connections. The outcomes of this research endeavor hold the potential to furnish practical insights for the formulation of targeted safety enhancement policies by governmental bodies or relevant agencies. [ABSTRACT FROM AUTHOR]

Copyright of Advances in Atmospheric Sciences is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The wideband absorption and transmission of frequency-selective rasorber (FSR) remain a persistent challenge in the application of radar devices. In this article, a novel high performance wideband FSR design based on cross bend resonators was proposed. The FSR consists of an upper absorption lossy layer, which offers broad absorption and transmission bands, and a lower bandpass frequency-selective surface that enables a highly selective transmission of incident electromagnetic wave. Full wave simulation results showed that this novel design achieves an absorption bandwidth of 83.7% with more than 90% absorptivity in the frequency range of 5.2–12.7 GHz. Furthermore, the passband's fractional bandwidth for the insertion loss (IL) less than −3 dB is 33.9%, ranging from 14.9 to 21 GHz, with the minimum IL recorded at 0.69 dB at 17.7 GHz. To further verify the proposed method, a prototype FSR with 10 × 10 units of 120 mm × 120 mm was fabricated and the performance of the FSR was tested. The experiment results were in good agreement with the simulated results, and it showed a significant monostatic radar cross-section reduction in the frequency range of 5.3 GHz to 18.3 GHz compared with a metallic plane of the same size. [ABSTRACT FROM AUTHOR]

Mars' polar ice deposits are thought to preserve a record of climate throughout their evolution. In addition to the large north polar layered deposits (NPLD) at Mars' north pole, smaller ice deposits are preserved in craters nearby. These outlying deposits were potentially formed by the same mechanisms that drive NPLD formation, or may represent more local mechanisms. Distinguishing between these possibilities would help elucidate the spatial homogeneity of Martian climate processes. Here, we analyzed SHARAD radar depth profiles from 34 locations across the NPLD and 5 locations within the Korolev crater ice deposit using Fourier transform analysis and dynamic time warping to quantitatively assess the similarity between the internal layered stratigraphy of the two deposits. We identify broad stratigraphic similarities between the Korolev deposit and the NPLD, suggesting they likely formed due to the same climate forcing mechanism, with local variability also observed across the NPLD. Plain Language Summary: Mars has two large ice caps at its poles, which combined contain a similar volume of ice to Greenland on Earth. Near these large ice caps are craters that are also filled with ice, which may or may not have formed due to the same mechanisms that formed the large neighboring ice cap. Ground penetrating radar observations of ice on Mars can allow us to see layers of ice and dust that are present throughout the interior of these deposits, and represent climate events that have taken place during the deposit's formation and evolution. We analyze radar observations from two deposits near Mars' north pole and quantitatively show that these deposits have a similar pattern of layering. These results could indicate that they have shared a similar climate history, therefore implying the importance of regional‐scale climate processes on Mars in addition to local processes for forming these ice deposits. Key Points: We use Fourier transform analysis and dynamic time warping to assess the similarities between two ice deposits near Mars' north poleWe identified a periodic signal with an average wavelength of ∼45 m in radar observations of the ice mound in Korolev crater and the NPLDWe identify similar broad climate forcing for both Korolev and NPLD ice, with local variability across the NPLD also observed [ABSTRACT FROM AUTHOR]

The evolution of charge structure involved in lightning discharge of a thunderstorm over the central Tibetan Plateau is investigated for the first time, based on the data from very high frequency interferometer, radar and sounding. During the developing‐mature stage, the TP thunderstorm exhibited a tripolar charge structure evolved from an initial inverted dipole. At the mature stage, a bottom‐heavy tripole charge structure is clearly presented, with a strong lower positive charge center (LPCC) at temperatures above −10°C, a middle negative charge region between −30°C and −15°C, and an upper positive charge region at T < −30°C. As the LPCC was depleted, the charge structure evolved into a normal tripole with a pocket LPCC. The merging between different convective cells resulted in the formation of two adjacent negative charge regions located directly and obliquely above the LPCC, and horizontally arranged different charge regions were simultaneously involved in the same lightning discharge. Plain Language Summary: Tibetan Plateau thunderstorms usually exhibit special convective structures. Using the data from the accurate lightning VHF interferometer, electric field mill, fast/slow antenna and C‐band radar, the evolution of the charge structure of thunderstorms and their influence on lightning discharges are investigated. Our observation for the first time revealed the charge structure evolution of the central‐TP thunderstorm which involved in the lightning discharge, exhibiting a bottom heavy tripole charge structure with a large LPCC in the mature stage evolved from an initial inverted dipole and the usual tripole in the dissipating stage of the thunderstorm. Under different magnitudes of the LPCC, different types of lightning discharges including ‐IC, +IC and ‐CG flashes were generated, indicating the crucial effects of LPCC on the lightning discharge types. Key Points: The charge structure of the TP thunderstorm evolves from an initial inverted dipole to a mature stage tripole with a strong LPCCHorizontally distributed negative charge zones from cell merger are simultaneously involved in the discharge of a single lightning flashDifferences in the relative magnitude of LPCC leads to various types of lightning discharges [ABSTRACT FROM AUTHOR]

Monitoring the generation and movement of equatorial plasma bubbles (EPBs) in a large longitude region is crucial important for better understanding their day‐to‐day variability. Using the newly developed Low lAtitude long Range Ionospheric raDar (LARID) at Dongfang (19.2°N, 108.8°E, dip lat. 13.8°N), China, an extremely long‐range experiment for observing EPB irregularities in a range of ±9,600 km to the radar site was first carried out. The results show that EPB irregularities with ranges up to 7,000 and 9,500 km were observed by the east and west beams of LARID, respectively. By incorporating simultaneous observations from GNSS receiver and ionosonde networks, it is demonstrated that the EPBs generated from post‐sunset to sunrise over a very wide longitude of ∼140°, from Pacific to Africa could be observed by LARID. The results, for the first time, demonstrate the possibility for tracing global EPBs in real time using a few low latitude over‐the‐horizon radars. Plain Language Summary: Equatorial plasma bubble (EPB), which can cause severe ionospheric scintillation, is an important space weather phenomenon. The occurrence of EPBs exhibits complex longitude variation characteristics. Due to the fact that most of the equatorial and low latitude region is covered by ocean, it is challenging to monitor the generation and movement of global EPBs. Recently, an over‐the‐horizon (OTH) radar at low latitude, that is, the LARID, has been built for observing EPB irregularities. However, it is not clear that how far an OTH radar at low latitude can observe irregularities. This would be very important in the design of a low latitude OTH radar network for tracing global EPB irregularities. To address this issue, an extremely long‐range experiment covering a wide longitude of about 180° was performed for the first time with LARID. The successful observation of EPB irregularities from Pacific to Africa sectors demonstrates the possibility of monitoring the complex longitudinal variations of EPBs by an OTH radar, even during geomagnetic storms. The results provide meaningful insight for building a low latitude OTH radar network in future, that consists of three to four OTH radars could have the capability to obtain global EPBs in real time. Key Points: First extremely long‐range experiment for observing equatorial plasma bubbles over a large longitude was conductedEquatorial plasma bubbles with ranges as far as 9,500 km were successfully observed by an over‐the‐horizon radarThe results demonstrate the capability for tracing global equatorial plasma bubbles using a few low latitude over‐the‐horizon radars [ABSTRACT FROM AUTHOR]

Measurements with a 763 nm channel, located within the oxygen A-band and equipped on the Second-generation Global Imager (SGLI) onboard the JAXA's Global Change Observation Mission – Climate (GCOM-C) satellite, have the potential to retrieve cloud base height (CBH) and cloud geometric thickness (CGT) through passive remote sensing. This study implemented an algorithm to retrieve the CBH using the SGLI 763 nm channel in combination with several other SGLI channels in the visible, shortwave infrared, and thermal infrared regions. In addition to CBH, the algorithm can simultaneously retrieve other key cloud properties, including cloud optical thickness (COT), cloud effective radius, ice COT fraction as the cloud thermodynamic phase, cloud top height (CTH), and CGT. Moreover, the algorithm can be seamlessly applied to global clouds comprised of liquid, ice, and mixed phases. The SGLI-retrieved CBH exhibited quantitative consistency with CBH data obtained from the ground-based ceilometer network, ship-borne ceilometer, satellite-borne radar and lidar observations, as evidenced by sufficiently high correlations and small biases. These results provide practical evidence that the retrieval of CBH is indeed possible using the SGLI 763 nm channel. Moreover, the results lend credence to the future use of SGLI CBH data, including the estimation of the surface downward longwave radiative flux from clouds. Nevertheless, issues remain that must be addressed to enhance the value of SGLI-derived cloud retrieval products. These include the systematic bias of SGLI CTH related to cirrus clouds and the bias of SGLI CBH caused by multi-layer clouds. [ABSTRACT FROM AUTHOR]

The advent of deep learning has propelled significant advancements in object detection, thereby enhancing the intelligence of underwater autonomous driving systems. In this paper, we explore the cutting-edge applications of autonomous driving technology in the field of underwater exploration, addressing the pivotal role of target detection in navigating and executing tasks within challenging marine environments. In this study, the object detection capability of such systems is enhanced by integrating deep learning and multisensor fusion technology, especially by combining high-precision sensor data with multitask learning models to achieve efficient and robust detection. Our study has three principal contributions. First, we introduce a novel light perception detection system that combines monocular camera technology with 4D radar. It enriches environmental perception by weaving in radar signals and significantly enhances the accuracy and stability of target detection. Second, we have developed a dual-modal detection framework, named Radar-Picture Detection, which utilizes a parallel sequence prediction method. This approach prioritizes radar signal processing, aiding in the improvement of target detection accuracy in intricate underwater environments. Third, we conducted a comprehensive evaluation of our model's performance using the FloW Dataset, which is specifically curated for identifying floating waste in inland waters through unmanned vessel footage. We not only propel forward the field of target detection for underwater autonomous systems but also establish new avenues and a solid foundation for deploying deep learning and multisensor fusion technology in marine environmental perception. Insights and methodologies from this study are poised to spearhead further developments in autonomous marine exploration, enhancing safety, efficiency, and our understanding of underwater environments. [ABSTRACT FROM AUTHOR]

In recent years, the application of compact and cost-effective deployable X-band polarimetric radars has gained in popularity, particularly in regions with complex terrain. The deployable radars generally use a radome constructed by joining multiple panels using metallic threads to facilitate easy transportation. As a part of the ESPOIRS project, Laboratoire de l'Atmosphεave;re et des Cyclones has acquired an X-band meteorological radar with four panel radome configuration. In this study, we investigated the effect of the radome on the measured polarimetric variables, particularly differential reflectivity and differential phase. Our observations reveal that the metallic threads connecting the radome panels introduce power loss at vertical polarization, leading to a positive bias in the differential reflectivity values. To address the spatial variability bias observed in differential reflectivity and differential phase, we have developed a novel algorithm based on the Discrete Fourier Transform. The algorithm's performance was tested during an intense heavy rainfall event caused by the Batsirai cyclone on Reunion Island. The comparative and joint histogram analysis demonstrates the algorithm's effectiveness in correcting the spatial bias in the polarimetric variables. [ABSTRACT FROM AUTHOR]

Copyright of Machine Tool & Hydraulics is the property of Guangzhou Mechanical Engineering Research Institute (GMERI) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

An experimental study aimed at identifying special rainfall regimes with the help of co-located disdrometers is performed. Eight potentially special events (i.e., four number-controlled events and four size-controlled events) are identified and examined. However, a detailed cross-check with additional, independent radar measurements reveals no clear evidence of special rainfall dynamics. The research underscores the difficulty of experimentally confirming seemingly straightforward questions about rainfall patterns and dynamics that have been theorized in the literature for several decades but never formally validated experimentally. The study also questions the reliability of previous claims and serves as a reminder to approach such problems with more caution, emphasizing the need for rigorous uncertainty analysis and multiple cross-checks between sensors to avoid misinterpretation. [ABSTRACT FROM AUTHOR]

In autonomous driving, Frequency-Modulated Continuous-Wave (FMCW) radar has gained widespread acceptance for target detection due to its resilience and dependability under diverse weather and illumination circumstances. Although deep learning radar target identification models have seen fast improvement, there is a lack of research on their susceptibility to adversarial attacks. Various spoofing attack techniques have been suggested to target radar sensors by deliberately sending certain signals through specialized devices. In this paper, we proposed a new adversarial deep learning network for spoofing attacks in radar target detection (RTD). Multi-level adversarial attack prevention using deep learning is designed for the coherence pulse deep feature map from DAALnet and Range-Doppler (RD) map from TDDLnet. After the discrimination of the attack, optimization of hybrid deep learning (OHDL) integrated with enhanced PSO is used to predict the range and velocity of the target. Simulations are performed to evaluate the sensitivity of AOHDL for different radar environment configurations. RMSE of AOHDL is almost the same as OHDL without attack conditions and it outperforms the earlier RTD implementations. [ABSTRACT FROM AUTHOR]

This paper considers a terahertz (THz) dual-function multi-input multi-output (MIMO) radar and communication system with the assistance of a reconfigurable intelligent surface (RIS) and jointly designs the constant modulus (CM) waveform and RIS phase shifts. A weighted optimization scheme is presented, to minimize the weighted sum of three objectives, including communication multi-user interference (MUI) energy, the negative of multi-target illumination power and the MIMO radar waveform similarity error under a CM constraint. For the formulated non-convex problem, a novel alternating coordinate descent (ACD) algorithm is introduced, to transform it into two subproblems for waveform and phase shift design. Unlike the existing optimization algorithms that solve each subproblem by iteratively approximating the optimal solution with iteration stepsize selection, the ACD algorithm can alternately solve each subproblem by dividing it into multiple simpler problems, to achieve closed-form solutions. Our numerical simulations demonstrate the superiorities of the ACD algorithm over the existing methods. In addition, the impacts of the weighting coefficients, RIS and channel conditions on the radar communication performance of the THz system are analyzed. [ABSTRACT FROM AUTHOR]

In-cabin occupancy detection has become increasingly important due to incidents involving children left in vehicles under extreme temperature conditions. Frequency modulated continuous wave (FMCW) radars are widely used for non-contact monitoring and sensing applications, particularly for occupancy detection. However, the confined and metallic environment inside vehicle cabins presents significant challenges due to multipath reflections. This paper introduces a novel approach that detects the occupied space in each seat to determine occupancy, using the variance of detected points as an indicator of volume occupancy. In an experimental study involving 70 different scenarios with single and multiple subjects, we classify occupants in each seat into one of three categories: adult, baby, or empty. The proposed method achieves an overall accuracy of 96.7% using an Adaboost classifier and a miss-detection rate of 1.8% for detecting babies. This approach demonstrates superior robustness to multipath interference compared to traditional energy-based methods, offering a significant advancement in in-cabin occupancy detection technology. [ABSTRACT FROM AUTHOR]

This paper introduces a resource management approach for distributed multiple-input multiple-output (MIMO) radar systems equipped with phased array antennas. The approach focuses and adjusts narrow beams from all transmit and receive nodes to generate a regularly shaped observation area for reliable detection. Based on this, a structured early warning framework can be built by evenly arranging sufficient observation areas to cover the surveillance region and periodically scanning these areas for continuous monitoring. Observation area generation, a core technique for this framework, involves the joint optimization of beamforming weights for both transmit and receive nodes, as well as the beam dwell time. Our optimization strategy is designed to achieve two key objectives: minimizing beam dwell time to ensure rapid alerts for defense systems, and minimizing node transmit power to extend operational time while reducing the risk of intercept. To address the problem of observation area generation, we propose a two-stage method. The first stage uses the signal-to-clutter-plus-noise ratio (SCNR) as a new criterion to determine the transmit and receive beamforming weights. The second stage employs a power factor as an additional variable to scale the transmit beamforming weights under various beam dwell times, constructing a Pareto solution set for the problem. Numerical simulations validate the effectiveness of our method, demonstrating improved detection capabilities compared to monostatic phased array radar systems. [ABSTRACT FROM AUTHOR]

The design and optimization of sensor array configurations is a significant challenge for distributed SAR-GMTI radar systems because the system performance of distributed array radar is a comprehensive result of several conflicting evaluation indicators. This paper developed a multi-objective intelligent optimization method to solve the global optimal problem of array configurations in terms of achieving optimal GMTI performance. Firstly, to formulate the relationship between array configuration and GMTI performance, we established three objective functions derived from evaluating indicators of SAR-GMTI performance. Specifically, in the objective functions, we proposed a novel clutter covariance matrix model that added several typical non-ideal factors of the real-world detection environment. This provides a way to build a bridge between the array configuration, environment clutter, and GMTI performance. Then, we proposed an improved multi-objective snake optimization algorithm (IMOSOA) that combined the Pareto optimization mechanism with snake optimization to solve the multi-objective optimization problem while reconciling the conflicts between different objective functions. Meanwhile, some significant improvements were made to speed up convergence. That is, tent chaotic mapping-based initialization, multi-group coevolution, and individual mutation strategies were applied to solve the non-convergence problem of global searching. Finally, in the case of an airborne SAR-GMTI system, numerical experiments demonstrated that the proposed IMOSOA has superior performance than other contrast methods, especially in terms of GMTI applications. [ABSTRACT FROM AUTHOR]

We focus on an integrated sensing and communication (ISAC) system—a single platform equipped with multiple antennas transmitting a waveform to detect targets and communicate with downlink users. Due to spectrum sharing between multiple-input–multiple-output (MIMO) radar and multiuser MIMO (MU-MIMO) communication, beamforming is becoming increasingly important as a technique that enables the creation of directional beams. In this paper, we propose a novel joint transmit beamforming design scheme that employs a beam pattern approximation strategy for radar sensing and utilizes rate-splitting for multiuser communication offering advanced interference management strategies. The optimization problems are formulated from both radar-centric and trade-off viewpoints. First, we propose a radar-centric beamforming scheme to achieve sensing efficiency through beam pattern approximation, while requiring the fairness signal-to-interference-plus-noise ratio (SINR) to be higher than a given threshold to guarantee a minimal level of communication quality, while the obtained performance for the communication system is limited in this scheme. To address this problem, we propose a beamforming design scheme from a trade-off viewpoint that flexibly optimizes both sensing and communication performances with a regularization parameter. Finally, we propose a partial rate-splitting-based beamforming design method aimed at maximizing the effective sensing power, with the constraint of a minimal sum rate for downlink users. Numerical results are provided to assess the effectiveness of all proposed schemes. [ABSTRACT FROM AUTHOR]

In radar High-Resolution Range Profile (HRRP) target recognition, the targets of interest are always non-cooperative, posing a significant challenge in acquiring sufficient samples. This limitation results in the prevalent issue of limited sample availability. To mitigate this problem, researchers have sought to integrate handcrafted features into deep neural networks, thereby augmenting the information content. Nevertheless, existing methodologies for fusing handcrafted and deep features often resort to simplistic addition or concatenation approaches, which fail to fully capitalize on the complementary strengths of both feature types. To address these shortcomings, this paper introduces a novel radar HRRP feature fusion technique grounded in the Feature Weight Assignment Generative Adversarial Network (FWA-GAN) framework. This method leverages the generative adversarial network architecture to facilitate feature fusion in an innovative manner. Specifically, it employs the Feature Weight Assignment Model (FWA) to adaptively assign attention weights to both handcrafted and deep features. This approach enables a more efficient utilization and seamless integration of both feature modalities, thereby enhancing the overall recognition performance under conditions of limited sample availability. As a result, the recognition rate increases by over 4% compared to other state-of-the-art methods on both the simulation and experimental datasets. [ABSTRACT FROM AUTHOR]

In this paper, a fast method to generate a constant-modulus (CM) waveform for a multiple-input, multiple-output, (MIMO) radar is proposed. To simplify the optimization process, the design of the transmit waveform is decoupled from the design of transmit beamformers (TBs) and subpulses. To further improve the computational efficiency, the TBs' optimization is conducted in parallel, and a linear programming model is proposed to match the desired beampattern. Additionally, we incorporate the perturbation vectors into the TBs' optimization so that the TBs can be adjusted to satisfy the CM constraint. To quickly generate the CM subpulses with the desired range-compression (RC) performance, the classical linear frequency modulation (LFM) signal and non-LFM (NLFM) are adopted as subpulses. Meanwhile, to guarantee the RC performance of the final angular waveform, the selection of LFM signal parameters is analyzed to achieve a low cross-correlation between subpulses. Numerical simulations verify the transmit beampattern performance, RC performance, and computational efficiency of the proposed method. [ABSTRACT FROM AUTHOR]

Cross-domain cooperative task allocation is a complex and challenging issue in the field of multi-agent task allocation that requires urgent attention. This paper proposes a task allocation method based on the multi-strategy improved dung beetle optimization (MSIDBO) algorithm, aiming to solve the problem of fully distributed multi-agent cross-domain cooperative task allocation. This method integrates two key objective functions: target allocation and control allocation. We propose a target allocation model based on the optimal comprehensive efficiency, cluster load balancing, and economic benefit maximization, and a control allocation model leveraging the radar detection ability and control data link connectivity. To address the limitations of the original dung beetle optimization algorithm in solving such problems, four revolutionary strategies are introduced to improve its performance. The simulation results demonstrate that our proposed task allocation algorithm significantly improves the cross-domain collaboration efficiency and meets the real-time requirements for multi-agent task allocation on various scales. Specifically, our optimization performance was, on average, 32.5% higher compared to classical algorithms like the particle swarm optimization algorithm and the dung beetle optimization algorithm and its improved forms. Overall, our proposed scheme enhances system effectiveness and robustness while providing an innovative and practical solution for complex task allocation problems. [ABSTRACT FROM AUTHOR]

The beat signal obtained from frequency-modulated continuous-wave (FMCW) radar is a waveform that is corrupted by noise and requires filtering out interference components for frequency calibration. Traditional FFT methods are affected by the fence effect and spectral leakage, leading to a reduction in frequency estimation accuracy. Therefore, an improved double-spectrum-line interpolation frequency estimation algorithm is proposed in this paper, utilizing spectral refinement and phase interpolation. Firstly, the post-FFT spectral signal is refined to narrow the frequency search range and enhance frequency resolution, thereby separating the noise signal. Then, a frequency deviation factor is defined based on the relationship between adjacent phase angles. Finally, the signal's phase angles are interpolated using the frequency deviation factor to estimate the frequency of the beat signal. Experimental results demonstrate that the proposed algorithm reduces the impact of quantization on the frequency distribution and increases the signal's noise resistance. The proposed algorithm has a higher accuracy and lower standard deviation compared to the recently proposed algorithm. [ABSTRACT FROM AUTHOR]