Search

To address the heterogeneous clutter environment of airborne early warning radar in space-time adaptive processing (STAP), a direct data domain (D3) STAP method via joint-sparse characteristics of clutter and noise is proposed. Firstly, training sample set is expanded using the sub-aperture smoothing technique. Then, the STAP power dictionary matrix is constructed based on the second-order characterization theory of the clutter spectrum, and the objective function in derived. Meanwhile, the space-time power spectrum of the signal in the cell under test is solved. Finally, the clutter plus noise covariance matrix without aperture loss is reconstructed according to the clutter prior information. Numerical experiments show that the estimation accuracy of the proposed method is higher than that of the traditional sparse recovery D3-STAP algorithm, and the proposed method has better performance of the low-speed target detection under ideal conditions and array errors. [ABSTRACT FROM AUTHOR]

Aiming at the low observable target detection under sea clutter backgrounds, this paper emphasizes the exploration of distinguishable full-polarization features between target and sea clutter echoes. To overcome the shortcomings of the existing polarization feature-based methods, the full-polarization features of sea clutter are modeled and analyzed in detail by using Van Zyl polarization decomposition. Then, three polarimetric features (the relative surface scattering energy, the relative dihedral scattering energy and the relative diffuse scattering energy) are extracted from the fully polarimetric radar sea clutter echoes, which improve the feature differences between sea clutter and targets. And a tri-polarimetric feature detector with constant false alarm rate (CFAR) is constructed based on the fast convex hull learning algorithm. The experimental results on the real measured IPIX radar datasets prove that the proposed full-polarization feature detector obtains more competitive detection performance and lower computational complexity than the several existing feature-based detectors. [ABSTRACT FROM AUTHOR]

Bistatic radar exhibits spatial isomerism and diverse configurations, leading to unique clutter characteristics distinct from those of monostatic radar. The clutter rank serves as a pivotal indicator of clutter characteristics, enabling the quantification of clutter severity. Space-time adaptive processing (STAP) is a critical technique to detect moving targets, and clutter rank determines the number of independent and identically distributed (IID) training samples and the degree of freedom (DOF) for effective suppression of clutter that STAP requires. Therefore, the accurate estimation of clutter rank for bistatic radar can provide a crucial indicator for designing and constructing STAP processors, thereby facilitating fast and efficient clutter suppression in bistatic radar systems. This study is based on the idea that clutter rank is the number of prolate spheroidal wave function (PSWF) orthogonal bases utilized for approximating the clutter signal. Firstly, the challenge of utilizing PSWF orthogonal bases for approximating the clutter signal in bistatic radar is elucidated. This pertains to the fact that, unlike monostatic radar clutter, bistatic radar clutter is not capable of being expressed as a single-frequency signal. The clutter rank estimation for bistatic radar is thus derived as the frequency bandwidth estimation. Secondly, to achieve this estimation, the frequency distribution of each individual scattering unit is investigated, thereby determining their extending frequency broadening (EFB) as compared to that of single-frequency. Subsequently, the integral average of EFB across the entire range bin is computed, ultimately enabling the acquisition of bistatic radar's frequency bandwidth. Finally, the estimation method is extended to non-side-looking mode and limited observation areas with pattern modulation. Simulation experiments confirm that our proposed method provides accurate clutter rank estimations, surpassing 99% proportions of large eigenvalues across various bistatic configurations, observation modes, and areas. [ABSTRACT FROM AUTHOR]

This paper addresses polarimetric adaptive coherent detection of radar targets embedded in sea clutter. Initially, radar clutter data across multiple polarimetric channels is modeled using a compound Gaussian framework featuring an unspecified speckle covariance matrix and lognormal texture distribution. Subsequently, three adaptive polarimetric coherent detectors are derived, employing parameter estimation and two-step versions of the generalized likelihood ratio test (GLRT): the complex parameter Rao and Wald tests. These detectors utilize both clutter texture distribution information and radar data's polarimetric aspects to enhance detection performance. Simulation experiments demonstrate the superiority of three proposed detectors over the competitors, and that they are sensitive to polarimetric channel parameters such as secondary data quantity, target or clutter speckle correlation, and signal-to-clutter ratio disparity. Additionally, the proposed detectors exhibit a near-constant false alarm rate relative to average clutter power and speckle covariance matrix. [ABSTRACT FROM AUTHOR]

In tracking scenarios involving groups with dense targets, achieving effective data association is challenging due to mutual occlusion and interference among targets. The complexity of the tracking problem is further exacerbated in low‐observable environments by the increase in false alarm rates. The track‐before‐detect (TBD) is an advanced technology for detecting and tracking low‐observable targets, effectively mitigating data association problems by integrating multi‐frame echo data. However, the existing multi‐target TBD algorithms typically assume that the targets are spatially separated and are not suitable for scenarios involving group targets. A group target maximum‐likelihood probabilistic data association (GT‐ML‐PDA) algorithm, based on the concept of TBD, is proposed to track group targets effectively in low‐observable environments. The proposed algorithm divides group target tracking into two stages: group centre trajectory estimation and individual target trajectory estimation. To enhance the performance of the proposed algorithm, two strategies are suggested: modifying the equivalent measurements and extracting independent measurement sets for individual targets. Simulation results demonstrate that the proposed algorithm is capable of effectively tracking numerous individual targets within a group, even in the presence of heavy clutter. [ABSTRACT FROM AUTHOR]

The traditional sparse recovery (SR) space-time adaptive processing (STAP) algorithms are greatly affected by grid mismatch, leading to poor performance in airborne bistatic radar clutter suppression. In order to address this issue, this paper proposes an SR STAP algorithm for airborne bistatic radars based on atomic selection under the Bayesian framework. This method adopts the idea of atomic selection for the process of Bayesian inference, continuously evaluating the contribution of atoms to the likelihood function to add or remove atoms, and then using the selected atoms to estimate the clutter support subspace and perform sparse recovery in the clutter support subspace. Due to the inherent sparsity of clutter signals, performing sparse recovery in the clutter support subspace avoids using a massive number of atoms from an overcomplete space-time dictionary, thereby greatly improving computational efficiency. In airborne bistatic radar scenarios where significant grid mismatch exists, this method can mitigate the performance degradation caused by grid mismatch by encrypting grid points. Since the sparse recovery is performed in the clutter support subspace, encrypting grid points does not lead to excessive computational burden. Additionally, this method integrates out the noise term under a new hierarchical Bayesian model, preventing the adverse effects caused by inaccurate noise power estimation during iterations in the traditional SR STAP algorithms, further enhancing its performance. Our simulation results demonstrate the high efficiency and superior clutter suppression performance and target detection performance of this method. [ABSTRACT FROM AUTHOR]

Space–time adaptive processing (STAP) based on sparse recovery achieves excellent clutter suppression and target detection performance, even with a limited number of available training samples. However, most of these methods face performance degradation due to grid mismatch, which impedes their application in bistatic clutter suppression. Some gridless methods, such as atomic norm minimization (ANM), can effectively address grid mismatch issues, yet they are sensitive to parameter settings and array errors. In this article, the authors propose a data and model-driven algorithm that unfolds the iterative process of atomic norm minimization into a deep network. This approach establishes a concrete and systematic link between iterative algorithms, extensively utilized in signal processing, and deep neural networks. This methodology not only addresses the challenges associated with parameter settings in traditional optimization algorithms, but also mitigates the lack of interpretability issues commonly found in deep neural networks. Moreover, due to more rational parameter settings, the proposed algorithm achieves effective clutter suppression with fewer iterations, thereby reducing computational time. Finally, extensive simulation experiments demonstrate the effectiveness of the proposed algorithm in clutter suppression for airborne bistatic radar. [ABSTRACT FROM AUTHOR]

In recent years, the high-frequency hybrid sky-surface wave radar (HSSWR) has been increasingly used for target detection applications. Nevertheless, the specific bistatic system layout and the phase path disturbances induced by the ionospheric propagation channel may severely spread the sea clutter spectrum, thereby deteriorating the detection ability of the HSSWR for slow-moving targets. In this work, a novel subspace method based on the hybrid use of the amplitude and phase estimator (APES) and the orthogonal projection (OP) in the spatial–temporal domain, denoted as the APES-OP method, is proposed to suppress the spreading first-order sea clutter of the HSSWR. The distribution characteristics of targets and first-order sea clutter in the spatial–temporal domain were investigated, and a time-domain subspace signal model was adopted to describe targets perturbed by ionospheric phase path modulation. An APES filter was adopted to filter out the potential targets with a traversal approach to avoid attenuating desired signals while suppressing sea clutter. After that, sampling data from multi-channels and slow-time domains at the cell under test were employed to construct a spatial–temporal matrix, which was then utilized to obtain the sea clutter subspace by singular value decomposition. Simulation results indicate that the proposed algorithm can suppress sea clutter while retaining the target, even if the target is buried by sea clutter. The processing results of measured data further demonstrate the efficiency of the proposed algorithm. After sea clutter suppression, the target obscured by clutter can be revealed, and the signal-to-clutter ratio of the target is greatly improved. [ABSTRACT FROM AUTHOR]

Synthetic aperture radar (SAR) measures backscattering characteristics of man-made targets and has the advantage of not being affected by weather and time. For target recognition in SAR images, the target reconstruction results based on attribute scattering centres are introduced for decision fusion. In comparison with original images, the reconstructed targets better relieve corruptions caused by noises, clutters, etc. A deep learning model, i.e. ResNet, is adopted as a basic classifier to classify both original and reconstructed images. According to the energy relationship between the reconstruction target and residual, the noise level of the original SAR image is defined. Then, the adaptive weights of original and reconstructed images are determined and a weighted decision fusion process is conducted to combine decisions from both images to confirm the target label. The proposed method is tested based on MSTAR dataset, and experimental results show its effectiveness. [ABSTRACT FROM AUTHOR]

A complete framework of predicting the attributes of sea clutter under different operational conditions, specified by wind speed, wind direction, grazing angle, and polarization, is proposed for the first time. This framework is composed of empirical spectra to characterize sea-surface profiles under different wind speeds, the Monte Carlo method to generate realizations of sea-surface profiles, the physical-optics method to compute the normalized radar cross-sections (NRCSs) from individual sea-surface realizations, and regression of NRCS data (sea clutter) with an empirical probability density function (PDF) characterized by a few statistical parameters. JONSWAP and Hwang ocean-wave spectra are adopted to generate realizations of sea-surface profiles at low and high wind speeds, respectively. The probability density functions of NRCSs are regressed with K and Weibull distributions, each characterized by two parameters. The probability density functions in the outlier regions of weak and strong signals are regressed with a power-law distribution, each characterized by an index. The statistical parameters and power-law indices of the K and Weibull distributions are derived for the first time under different operational conditions. The study reveals succinct information of sea clutter that can be used to improve the radar performance in a wide variety of complicated ocean environments. The proposed framework can be used as a reference or guidelines for designing future measurement tasks to enhance the existing empirical models on ocean-wave spectra, normalized radar cross-sections, and so on. [ABSTRACT FROM AUTHOR]

Feature extraction from the normalized transformation domain is a key technique in target detection. Traditional normalization approaches assume that matrix elements follow a normal distribution, but any deviations from this assumption can lead to significant systematic errors. This article presents a novel method that modifies the normalization process in the fractional Fourier transform (FRFT) domain by incorporating a threshold mechanism to counteract the effects of non‐normal distributions. Three modified FRFT features are then extracted from this modified FRFT domain. Furthermore, a target detection method that utilizes these three adjusted features is proposed. Experimental results based on measured data indicate that the modified FRFT features exhibit superior classification capabilities for sea clutter and targets compared to the original ones. Additionally, the experiments also demonstrate that under the same conditions, the proposed detection method outperforms traditional FRFT feature detector and the tri‐feature based detector. [ABSTRACT FROM AUTHOR]

Plagued by heterogeneous clutter, it is a serious challenge for airborne radars to detect low-altitude, weak targets. To overcome this problem, a novel matrix information geometry detector for airborne multi-channel radar is proposed in this paper. The proposed detector applies the given steering vector and array structure information to the matrix information geometry detection method so that it can be used for space–time adaptive detection. While improving the detection performance, the matrix information geometry detector's original anti-clutter advantage is enhanced as well. The simulation experiment results indicate that the proposed detector has advantages in several of the properties related to space–time adaptive detection, while its computational complexity does not increase significantly. Moreover, experiment results based on the measured data verify the superior performance of the proposed method. Sea-detecting data-sharing-program data, mountaintop data, and phased-array radar data are employed to verify the performance advantage of the proposed method in heterogeneous clutter and the ability for weak target detection. [ABSTRACT FROM AUTHOR]

This paper presents an assessment of X-band radar's detection capability to monitor Small Garbage Islands (SGIs), i.e., floating aggregations of marine litter consisting chiefly of plastic, under changing sea states. For this purpose, two radar measurement campaigns were carried out with controlled releases at sea of SGI modules assembled in the laboratory. One campaign was carried out with a calm sea and almost no wind in order to determine the X-band radar system's detection capabilities in an ideal scenario, while the other campaign took place with rough seas and wind. An analysis of the data acquired during the campaigns confirmed that X-band radar can detect small aggregations of litter floating on the sea surface. To demonstrate the radar's ability to detect SGIs, a statistical analysis was carried out to calculate the probability of false alarm and the probability of detection for two releases at two different distances from the radar. For greater readability of this work, all of the results obtained are presented both in terms of radar intensity and in terms of the radar cross-section relating to both the targets and the clutter. Another interesting study that is presented in this article concerns the measurement of the speed of movement (drift) of the SGIs compared with the measurement of the speed of the surface currents provided at the same time by the radar. The study also identified the radar detection limits depending on the sea state and the target distance from the antenna. [ABSTRACT FROM AUTHOR]

Sparse recovery space–time adaptive processing (SR-STAP) technology improves the moving target detection performance of airborne radar. However, the sparse recovery method with a fixed dictionary usually leads to an off-grid effect. This paper proposes a STAP algorithm for airborne radar based on dictionary and clutter power spectrum joint correction (DCPSJC-STAP). The algorithm first performs nonlinear regression in a non-stationary clutter environment with unknown yaw angles, and it corrects the corresponding dictionary for each snapshot by updating the clutter ridge parameters. Then, the corrected dictionary is combined with the sparse Bayesian learning algorithm to iteratively update the required hyperparameters, which are used to correct the clutter power spectrum and estimate the clutter covariance matrix. The proposed algorithm can effectively overcome the off-grid effect and improve the moving target detection performance of airborne radar in actual complex clutter environments. Simulation experiments verified the effectiveness of this algorithm in improving clutter estimation accuracy and moving target detection performance. [ABSTRACT FROM AUTHOR]

Transient interference often submerges the actual targets when employing over-the-horizon radar (OTHR) to detect targets. In addition, modern OTHR needs to carry out multi-target detection from sea to air, resulting in the sparse sampling of echo data. The sparse OTHR signal will raise serious grating lobes using conventional methods and thus degrade target detection performance. This article proposes a modified Alternating Direction Method of Multipliers (ADMM)-Net to reconstruct the target and clutter spectrum of sparse OTHR signals so that target detection can be performed normally. Firstly, transient interferences are identified based on the sparse basis representation and then excised. Therefore, the processed signal can be seen as a sparse OTHR signal. By solving the Doppler sparsity-constrained optimization with the trained network, the complete Doppler spectrum is reconstructed effectively for target detection. Compared with traditional sparse solution methods, the presented approach can balance the efficiency and accuracy of OTHR signal spectrum reconstruction. Both simulation and real-measured OTHR data proved the proposed approach's performance. [ABSTRACT FROM AUTHOR]

In environments with a low signal-to-reverberation ratio (SRR) characterized by fluctuations in clutter number and distribution, particle filter-based tracking methods may experience significant fluctuations in the posterior probability of existence. This can lead to interruptions or even loss of the target trajectory. To address this issue, an adaptive PF-based tracking method (APF) with joint reverberation suppression is proposed. This method establishes the state space model under the Bayesian framework and implements it through particle filtering. To keep the weak target echoes, all the non-zero entries contained in the sparse matrix processed by the low-rank and sparsity decomposition (LRSD) are treated as the measurements. The prominent feature of this approach is introducing an adaptive measurement likelihood ratio (AMLR) into the posterior update step, which solves the problem of unstable tracking due to the strong fluctuation in the number of point measurements per frame. The proposed method is verified by four shallow water experimental datasets obtained by an active sonar with a uniform horizontal linear array. The results demonstrate that the tracking frame success ratio of the proposed method improved by over 14% compared with the conventional PF tracking method. [ABSTRACT FROM AUTHOR]

In cluttered electromagnetic environments, radar is often disturbed by varied clutter, making target detection challenging. Therefore, achieving effective clutter suppression is crucial for radar target detection. However, traditional clutter suppression methods face three key challenges: (1) significant degradation in target signal detection performance when the clutter's Doppler spectrum completely masks the target signal; (2) heavy reliance on prior knowledge for optimal performance; and (3) inherent signal energy loss during clutter suppression. To address these challenges, we propose a clutter suppression scheme based on blind source separation (BSS). Initially, the scheme utilizes parallel principal skewness analysis (PPSA) to process the echo signals in the range domain, which helps in identifying the position of moving targets. Subsequently, PPSA is applied once more to process the moving targets in the Doppler domain, allowing for the precise determination of their relative velocities. Subsequently, we evaluate the scheme's performance with simulated and real data, comparing it with traditional clutter suppression methods and other BSS techniques. The results confirm the effectiveness of the scheme in clutter suppression. [ABSTRACT FROM AUTHOR]

In the field of radar target detection, the conventional approach is to employ the range profile energy accumulation method for detecting extended targets. However, this method becomes ineffective when dealing with non-stationary and non-uniform radar clutter scenarios, as well as long-distance targets with weak radar cross sections (RCSs). In such cases, the signal-to-noise ratio (SNR) of the target echo is severely degraded, rendering the energy accumulation detection algorithm unreliable. To address this issue, this paper presents a new extended target detection method based on the maximum eigenvalue of the Hermitian matrix. This method utilizes a detection model that incorporates observed data and employs the likelihood ratio test (LRT) theory to derive the maximum eigenvalue detector at low SNR. Specifically, the detector constructs a matrix using a sliding window block with the available data and then computes the maximum eigenvalue of the covariance matrix. Subsequently, the maximum eigenvalue matrix is transformed into a one-dimensional eigenvalue image, enabling extended target detection through analogy with the energy accumulation detection method. Furthermore, this paper analyzes the proposed extended target detection method from both theoretical and experimental perspectives, validating it through field-measured data. The results obtained from the measured data demonstrate that the method effectively enhances the SNR in low SNR conditions, thereby improving target detection performance. Additionally, the method exhibits robustness across different scattering center targets. [ABSTRACT FROM AUTHOR]

An airborne bistatic radar working in downward-looking mode confronts two major challenges for low-altitude target detection. One is range cell migration (RCM) and Doppler migration (DM) resulting from the relative motion of the radar and target. The other is the non-stationarity characteristic of clutter due to the radar configuration. To solve these problems, this paper proposes a joint implementation method based on sub-aperture processing to achieve clutter suppression and coherent maneuvering target detection. Specifically, clutter Doppler compensation and sliding window processing are carried out to realize sub-aperture space–time processing, removing the clutter non-stationarity resulting from the bistatic geometric configuration. Thus, the output matrix of clutter suppression in the sub-aperture could be obtained. Then, the elements with the same phase of this matrix are superimposed and rearranged to achieve the reconstructed 2-D range-pluse echo matrix. Next, the aperture division with respect to slow time is conducted and the RCM correction based on modified location rotation transform (MLRT) and coherent integration (CI) are realized within each sub-aperture. Finally, the matched filtering process (MFP) is applied to compensate for the RCM/DM among different sub-apertures to coherently integrate the maneuvering target energy of all sub-apertures. The simulation and measured data processing results prove the validity of the proposed method. [ABSTRACT FROM AUTHOR]

High-power large-aperture radar instruments are capable of detecting thousands of meteor head echoes within hours of observation, and manually identifying every head echo is prohibitively time-consuming. Previous work has demonstrated that convolutional neural networks (CNNs) accurately detect head echoes, but training a CNN requires thousands of head echo examples manually identified at the same facility and with similar experiment parameters. Since pre-labeled data is often unavailable, a method is developed to simulate head echo observations at any given frequency and pulse code. Real instances of radar clutter, noise, or ionospheric phenomena such as the equatorial electrojet are additively combined with synthetic head echo examples. This enables the CNN to differentiate between head echoes and other phenomena. CNNs are trained using tens of thousands of simulated head echoes at each of three radar facilities, where concurrent meteor observations were performed in October 2019. Each CNN is tested on a subset of actual data containing hundreds of head echoes, and demonstrates greater than 97% classification accuracy at each facility. The CNNs are capable of identifying a comprehensive set of head echoes, with over 70% sensitivity at all three facilities, including when the equatorial electrojet is present. The CNN demonstrates greater sensitivity to head echoes with higher signal strength, but still detects more than half of head echoes with maximum signal strength below 20 dB that would likely be missed during manual detection. These results demonstrate the ability of the synthetic data approach to train a machine learning algorithm to detect head echoes. [ABSTRACT FROM AUTHOR]

The use of multiple synthetic aperture radar polarizations can improve biomass estimations compared to using a single polarization. In this study, we compared predictions of aboveground biomass change from ALOS-2 PALSAR-2 backscatter using linear regression based on (1) the cross-polarization channels, (2) the co- and cross-polarizations from fully polarimetric SAR, (3) Freeman–Durden polarimetric decomposition, and (4) the polarimetric radar vegetation index (RVI). Additionally, the impact of forest structure on the sensitivity of the polarimetric backscatter to AGB and AGB change was assessed. The biomass consisted of mainly coniferous trees at the hemi-boreal test site Remningstorp, located in southern Sweden. We found some improvements in the predictions when quad-polarized data (RMSE = 79.4 tons/ha) were used instead of solely cross-polarized data (RMSE = 84.9 tons/ha). However, when using Freeman–Durden decomposition, the prediction accuracy improved further (RMSE = 69.7 tons/ha), and the highest accuracy was obtained with the radar vegetation index (RMSE = 50.4 tons/ha). The corresponding R2 values ranged from 0.45 to 0.82. The bias was less than 1 t/ha for all models. An analysis of forest variables showed that the sensitivity to AGB was reduced for high values of basal-area-weighted mean height, basal area, and stem density when predicting absolute AGB, but the best change prediction model was sensitive to changes larger than the apparent saturation point for AGB state estimates. We conclude that by using fully polarimetric SAR images, forest biomass changes can be estimated more accurately compared to using single- or dual-polarization images. The results were improved the most (in terms of RMSE and R2) by using the Freeman–Durden decomposition model or the RVI, which captured especially the large changes better. [ABSTRACT FROM AUTHOR]

Multiple Large‐Scale Traveling Ionospheric Disturbances (LSTIDs) are observed in the European sector in both day‐time and night‐time during the magnetic storm on March 23–24, 2023. The Total Electron Content (TEC) observation from a network of GNSS receivers shows the propagation of LSTIDs with amplitudes between around 0.5 and 1 TECU originating from auroral and polar cusp regions down to southern Europe (35°N) with velocities between around 500 and 1,600 [m/s]. We study the energy deposition to the LSTIDs in the source regions and the resulting horizontal propagation over storm‐time background density by using continuous measurements of EISCAT incoherent scatter radars in northern Norway and Svalbard that allow for estimating the source energy to the thermosphere‐ionosphere system via Joule heating and particle precipitation. Both EISCAT and GNSS TEC data show that the electron density decreased to 50% in the auroral zone after the storm onset. The ionospheric heating caused a nearly 250% increase in the electron temperature above 200 km altitude and the ion temperature above 100 km altitude. We find that Joule Heating acts as a primary energy source for the night‐time LSTIDs triggered in the auroral region, while the day‐time LSTIDs can be also driven by precipitating particles in the polar cusp. We also find that a significant background density decrease over the whole European sector is caused by this storm for the following day, during which almost no clear LSTIDs are observed. Key Points: Multiple day‐time and night‐time LSTIDs are triggered at the high latitudes and propagated over Europe down to 35 N on March 23–24, 2023We evaluate source energy deposition of LSTIDs from Joule heating and particle precipitation using TEC and incoherent scatter radar dataJoule heating has more contribution to LSTID generation than particle precipitation during night‐time compared to day‐time [ABSTRACT FROM AUTHOR]

A sparse dictionary reconstruction algorithm based on grid selection is introduced to solve the grid mismatch when using the sparse recovery space time adaptive processing (SR-STAP) algorithm. First, the atom most closely related to clutter is selected from the traditional dictionary through the spectral value dimensionality reduction method. The local mesh is divided around the selected atoms to create mesh cells, and the mesh cells that are most likely to appear in the real clutter points are judged according to the local selection iteration criteria. In this way, the mesh spacing is refined, the local mesh selection is carried out step by step, and the optimal atoms in the local region are constantly adjusted and selected to narrow the search region until the iteration termination condition is met. Finally, the space-time plane is divided using a novel meshing technique that centers around the optimal atom. By removing atoms beyond the maximum range of spatial and Doppler frequencies, the simplified sparse dictionary can overcome the mesh mismatch problem. The simulation results demonstrate that the algorithm enhances the sparse recovery accuracy of clutter space-time spectrum, mitigates the mesh mismatch effect, and boosts STAP performance. [ABSTRACT FROM AUTHOR]

The presented paper is further focused on the presentation and subsequent assessment of utilising a proposed Neural Network (NN) with simple architecture in the role of a signal preprocessing algorithm for the Constant False Alarm Rate detector and the fixed threshold detector applied on a Range‐Doppler (RD) map with the aim of radar clutter impact reduction and minimisation of processing time. Based on a comparison of all tested algorithm results, it is possible to state that utilising the proposed NN with simple architecture led to reducing the impact of radar clutter when detecting radar targets on RD maps created from provided datasets. Comparing the mean processing time tmean values of all tested algorithms, the authors can state that employing the proposed NN in combination with the fixed threshold detector led to a significant improvement in the computation time needed for processing one RD map while preserving the suppression of radar clutter and detection of the radar targets. [ABSTRACT FROM AUTHOR]

In the field of meteorology, the global radar network is indispensable for detecting weather phenomena and offering early warning services. Nevertheless, radar data frequently exhibit anomalies, including gaps and clutter, arising from atmospheric refraction, equipment malfunctions, and other factors, resulting in diminished data quality. Traditional radar blockage correction methods, such as employing approximate radial information interpolation and supplementing missing data, often fail to effectively exploit potential patterns in massive radar data, for the large volume of data precludes a thorough analysis and understanding of the inherent complex patterns and dependencies through simple interpolation or supplementation techniques. Fortunately, edge computing possesses certain data processing capabilities and cloud center boasts substantial computational power, which together can collaboratively offer timely computation and storage for the correction of radar beam blockage. To this end, an edge-cloud collaborative driven deep learning model named DenMerD is proposed in this paper, which includes dense connection module and merge distribution (MD) unit. Compared to existing models such as RC-FCN, DenseNet, and VGG, this model greatly improves key performance metrics, with 30.7 % improvement in Critical Success Index (CSI), 30.1 % improvement in Probability of Detection (POD), and 3.1 % improvement in False Alarm Rate (FAR). It also performs well in the Structure Similarity Index Measure (SSIM) metrics compared to its counterparts. These findings underscore the efficacy of the design in improving feature propagation and beam blockage accuracy, and also highlights the potential and value of mobile edge computing in processing large-scale meteorological data. [ABSTRACT FROM AUTHOR]

The clutter suppression effect of ground objects significantly impacts the detection and tracking performance of avian lidar on low-altitude bird flock targets. It is imperative to simulate the point cloud data of ground objects in lidar to explore effective methods for suppressing clutter caused by ground objects in avian lidar. The traditional ray-tracing method is enhanced in this paper to efficiently obtain the point cloud simulation results of ground objects. By incorporating a beam constraint and a light-energy constraint, the screening efficiency of effective rays is improved, making them more suitable for simulating large scenes with narrow lidar beams. In this paper, a collision detection scheme is proposed based on beam constraints, aiming to significantly enhance the efficiency of ray-tracing collision detection. The simulation and experimental results demonstrate that, in comparison with other conventional simulation methods, the proposed method yields the point cloud results of ground objects that exhibit greater conformity to the actual lidar-collected point cloud results in terms of shape characteristics and intensity features. Additionally, the simulation speed is significantly enhanced. [ABSTRACT FROM AUTHOR]

Weather radar is a crucial tool in rainfall estimation, providing high-resolution estimates in both space and time. Despite this, radar rainfall estimates are subject to many error sources – including attenuation, ground clutter, beam blockage and the drop-size distribution – with the true rainfall field unknown. A flexible stochastic model for simulating errors relating to the radar rainfall estimation process is implemented, inverting standard weather radar processing methods, imposing path-integrated attenuation effects, a stochastic drop-size distribution field, along with sampling and random errors. This can provide realistic weather radar images, of which we know the true rainfall field, and the corrected 'best guess' rainfall field which would be obtained if they were observed in the real-world case. The structure of these errors is then investigated, with a focus on frequency and behaviour of 'rainfall shadows'. Half of simulated weather radar images have at least 3 % of significant rainfall rates shadowed and 25 % had at least 45 km2 containing rainfall shadows, resulting in underestimation of potential impacts of flooding. A model framework for investigating the behaviour of errors relating to the radar rainfall estimation process is demonstrated, with the flexible and efficient tool performing well at generating realistic weather radar images visually, for a large range of event types. [ABSTRACT FROM AUTHOR]

The non-stationary clutter of space-based early warning radar (SBEWR) is more serious than that of airborne early warning radar. This phenomenon is primarily attributed to the Earth's rotation and range ambiguity. The increase in clutter degrees of freedom (DOFs) and the significant widening of the clutter suppression notch are not conducive to moving target detection near main lobe clutter. This paper proposes an effective approach to suppress non-stationary clutter based on an interpulse multi-frequency mode for SBEWR. Using the orthogonality of the uniform stepping frequency signal, partial range ambiguity can be effectively suppressed, and the clutter DOFs will be reduced. Subsequently, joint pitch-azimuth-Doppler three-dimensional spacetime adaptive processing and slant range preprocessing are used to perform clutter suppression. This combination not only curtails the estimation error associated with the clutter covariance matrix but also enhances the overall detection capabilities of the system. The simulation results verify the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Millimeter-wave radar and optical cameras are one of the primary sensing combinations for autonomous platforms such as self-driving vehicles and disaster monitoring robots. The millimeter-wave radar odometry can perform self-pose estimation and environmental mapping. However, cumulative errors can arise during extended measurement periods. In particular scenes where loop closure conditions are absent and visual geometric features are discontinuous, existing loop detection methods based on back-end optimization face challenges. To address this issue, this study introduces a correlative scan matching (CSM) pose estimation method that integrates visual and radar line features (VRL-SLAM). By making use of the pose output and the occupied grid map generated by the front end of the millimeter-wave radar's simultaneous localization and mapping (SLAM), it compensates for accumulated errors by matching discontinuous visual line features and radar line features. Firstly, a pose estimation framework that integrates visual and radar line features was proposed to reduce the accumulated errors generated by the odometer. Secondly, an adaptive Hough transform line detection method (A-Hough) based on the projection of the prior radar grid map was introduced, eliminating interference from non-matching lines, enhancing the accuracy of line feature matching, and establishing a collection of visual line features. Furthermore, a Gaussian mixture model clustering method based on radar cross-section (RCS) was proposed, reducing the impact of radar clutter points online feature matching. Lastly, actual data from two scenes were collected to compare the algorithm proposed in this study with the CSM algorithm and RI-SLAM.. The results demonstrated a reduction in long-term accumulated errors, verifying the effectiveness of the method. [ABSTRACT FROM AUTHOR]

The detection of an infrared small target faces the problems of background interference and non-obvious target features, which have yet to be efficiently solved. By employing the non-local self-correlation characteristic of the infrared images, the principle component pursuit (PCP)-based methods are demonstrated to be applicable to infrared small target detection in a complex scene. However, existing PCP-based methods heavily depend on the uniform distribution of the background pixels and are prone to generating a high number of false alarms under strong clutter situations. In this paper, we propose a group low-rank regularized principle component pursuit model (GPCP) to solve this problem. First, the local image patches are clustered into several groups that correspond to different grayscale distributions. These patch groups are regularized with a group low-rank constraint, enabling an independent recovery of different background regions. Then, GPCP model integrates the group low-rank components with a global sparse component to extract small targets from the background. Different singular value thresholds can be exploited for image groups corresponding to different brightness and grayscale variance, boosting the recovery of background clutters and also enhancing the detection of small targets. Finally, a customized optimization approach based on alternating direction method of multipliers is proposed to solve this model. We set three representative detection scenes, including the ground background, sea background and sky background for experiment analysis and model comparison. The evaluation results show the proposed model has superiority in background suppression and achieves better adaptability for different scenes compared with various state-of-the-art methods. [ABSTRACT FROM AUTHOR]

A simple model is proposed for the radar returns from a slow‐moving target with a wake on the sea surface, based on measurements of returns from a submarine mast. This model is used to investigate the effect of a wake with a broad Doppler spectrum on the detection of a slow‐moving target in sea clutter. It is shown how the relative magnitudes of the returns from the target and its wake affect detection performance. Non‐coherent and coherent detectors are compared and it is shown that in some circumstance non‐coherent detectors may give better performance, dependent on the choice of radar waveform parameters. [ABSTRACT FROM AUTHOR]

Numerous visualisation methods have been proposed, including Origin-Destination maps to represent movement patterns gathered from social media; however, visual clutter remains a persistent issue due to complex data dimensionality. Besides, most Origin-Destination maps fail to illustrate the temporal dimension of social network phenomena within the geographical environment. To tackle this issue, we propose the visualisation method for geo-located Facebook social-media data while emphasising the time aspect. Based on the citizen-generated data for the European Union (EU), we estimated the EU citizens' residing or travelling across the EU member states as a means of current and previous destinations to reveal the extent of the hypothetical human migration. The proposed methodology consists of Origin-Destination maps implemented within the time geography framework as a model to support the process of analysis for decision-making. The generated visualisation allows comprehension of the scale of human movement distribution internally within the EU from a space--time perspective. [ABSTRACT FROM AUTHOR]

Keypoint detection plays a pivotal role in three-dimensional computer vision, with widespread applications in improving registration precision and efficiency. However, current keypoint detection methods often suffer from poor robustness and low discriminability. In this study, a novel keypoint detection approach based on the local variation of surface (LVS) is proposed. The LVS keypoint detection method comprises three main steps. Firstly, the surface variation index for each point is calculated using the local coordinate system. Subsequently, points with a surface variation index lower than the local average are identified as initial keypoints. Lastly, the final keypoints are determined by selecting the minimum value within the neighborhood from the initial keypoints. Additionally, a sampling consensus correspondence estimation algorithm based on geometric constraints (SAC-GC) for efficient and robust estimation of optimal transformations in correspondences is proposed. By combining LVS and SAC-GC, we propose a coarse-to-fine point cloud registration algorithm. Experimental results on four public datasets demonstrate that the LVS keypoint detection algorithm offers improved repeatability and robustness, particularly when dealing with noisy, occluded, or cluttered point clouds. The proposed coarse-to-fine point cloud registration algorithm also exhibits enhanced robustness and computational efficiency. [ABSTRACT FROM AUTHOR]

Traffic participant classification is crucial in autonomous driving perception. Millimeter wave radio detection and ranging radar is a cost-effective and powerful method to perform the task in adverse traffic scenarios, especially in bad inclement weather (e.g. fog, snow and rain) and poor lighting conditions. This paper presents an intelligent target classification algorithm for 77G radar based on correction data set. First, in order to handle the problem that the original data set may easily be interfered by obstacles, the angle information is filtered by analyzing the spatial information of radar signals, which means the interference clutter of obstacles can be effectively removed. Second, the primary data set is corrected using the significant difference between the micro-Doppler of the human body and car. Finally, the characteristic information of the radar signal is extracted, including distance, speed, orientation, micro-Doppler and reflection intensity, and the obtained data sets containing three types of targets (vehicles, human bodies and obstacles) are generated. The generated dynamic and static data sets are collected by sufficient experiments to construct deep learning classification models. The results show that the classification accuracy is improved by the measure of data set correction. [ABSTRACT FROM AUTHOR]

Due to limitations in computing power, the passive radar based on GNSS signal may not be able to use all the GNSS signals, requiring to make signal selection. Based on CRLB, a new radiation sources adaptive selection method for passive radar is proposed. With the goal of minimizing the CRLB of GNSS signals and the number of GNSS signals as constraints, an adaptive selection model is established. Simulation experiments were conducted using the selection model and particle filter algorithm. Experimental results show that the calculation accuracy of this algorithm is slightly lower than the tracking results obtained using all six satellites, but the calculation time is greatly reduced, reducing the requirement for radar computing power. [ABSTRACT FROM AUTHOR]

A great interest has been directed towards high frequency surface wave radar (HFSWR), because it is applied as long‐range early warning and real‐time measurements of sea surface condition tools in maritime surveillance. However, the unwanted reflection from the ionosphere (ionosphere clutter) damages the consistent detection performance of HFSWR. Target detection and environmental information extraction from radar echoes are primarily determined by the extent to which the medium contaminates the transmitted signals. Although the ionospheric signal corruption mechanisms are complex and various, a number of eliminating contamination approaches have been developed in the last 50 years. The purpose of this paper is providing an overview of the past and current clutter theoretical models and clutter suppression techniques for HFSWR system. The experimental results involved in the complex long‐term ionosphere observation and ionosphere echo analysis are first discussed. Special attention is paid to the correlation between the signal degradation sources and eliminating clutter techniques. Furthermore, the clutter suppression methods are classified based on the design idea. The specific clutter suppression methods as well as its operation principle are briefly introduced. Finally, several research trends and open issues are presented, with emphasis on the need for proactively controlling the radar design cost and generally agreed‐upon standards. [ABSTRACT FROM AUTHOR]

Moving aerial targets are tracked in new era of integrated computing with intelligent gadgets as well as systems. Traditional matched filter-based techniques estimation performance would suffer significantly when tracking multiple targets, as weak target is obscured by strong target or strong sidelobes. This exploration propose novel method in radar optical communication handling in light of flying objective examination by recurrence transfer speed with mess concealment. The conventional static radar-based Boundary Element mmwave signal bandwidth and frequency development is used here for the high-profile aerial target detection. Following that, adaptive processing based low rant spatio-temporal with MIMO (multiple input multiple output) is used to suppress radar clutter. Signal-to-clutter-plus-noise ratio (SCPNR), detection accuracy, traffic control, congestion control, and mAP (mean average precision) all play a role in the experimental analysis. Simulation results demonstrate proposed techniques efficacy. The proposed technique attained SCPNR of 85%, detection accuracy of 92%, traffic control of 53%, congestion controlof 55%, mAP of 55%. [ABSTRACT FROM AUTHOR]

Deep learning models such as deep convolutional neural networks (DCNNs) image classifiers have achieved outstanding performance over the last decade. However, these models are mostly trained with high-quality images drawn from publicly available datasets such as ImageNet. Recently, many researchers have evaluated the impact of low-quality image degradations on the performance of different neural network-based image classifiers. But, most of these studies generate low-quality images by synthetic modification of the high-quality images. Besides, most of the studies employed various image processing techniques to remove the image degradations and trained the DCNNs again to achieve better performance. But it has since been discovered that such methods could not improve the classification accuracy of DCNNs. The robustness of DCNNs based image classifiers trained on low-quality images resulting from natural factors common in autonomous driving and other intelligent system settings was rarely studied over the recent years. In this paper, we proposed a canonical correlation analysis (CCA) based fusion of camera and radar features for improving the performance of DCNNs image classifiers trained on natural adverse weather data. CCA is a statistical approach that creates a highly discriminative feature vector by measuring the linear relationship between the camera and radar features. A spatial attention network was designed to re-weight the camera features before associating them with radar features in the CCA-feature fusion block. Our findings based on experimental evaluations have proven that, indeed, the performance of the DCNN models (i.e., Alex-Net and VGG-16-Net) is heavily affected by degradations arising from natural factors. Specifically, the DCNN models are more affected by the degradations arising from rainfall, foggy and nighttime conditions using Radiate and Carrada datasets. However, the proposed fusion frameworks have improved the performance of the individual sensing modalities significantly. The radar data has helped substantially in enhancing the fusion performance, mainly using rainfall data where the camera data is heavily affected. [ABSTRACT FROM AUTHOR]

The detection of small targets within the background of sea clutter is a significant challenge faced in radar signal processing. Small target echoes are weak in energy, and can be submerged by sea clutter and sea spikes, which are caused by overturning waves and breaking waves. This severely affects the radar target detection performance. This paper proposes a smoothed pseudo-Wigner–Ville distribution–singular value decomposition (SPWVD-SVD) method for sea clutter suppression. This method determines the instantaneous frequency range of the target by contrasting the time–frequency characteristics of the sea spike and the target. Subsequently, it employs a singular value difference spectrum to reduce the rank of the Hankel matrix, thereby reducing the computational burden of the instantaneous frequency estimation step in the experiment. Based on the instantaneous frequency range of the target in the time–frequency domain, the singular values of the target signal are retained, while the singular values of clutter are set to zero. This process accomplishes the reconstruction of radar echo signals and effectively achieves the suppression of sea clutter. The suppression effect is verified using simulation data alongside ten sets of Intelligent Pixel processing X-band (IPIX) radar data against the background of sea spikes. By contrasting the clutter amplitudes before and after suppression, the SPWVD-SVD algorithm demonstrated an average clutter suppression of 15.06 dB, which proves the effectiveness of the proposed algorithm in suppressing sea clutter. [ABSTRACT FROM AUTHOR]

One of the most important problems in traffic management is evaluating the amount of traffic and the velocity of vehicles passing through transportation ways and main streets. In this study, the traffic load is measured, and the speed of cars is estimated using the 24 GHz FMCW radar. In this process, an antenna is installed to record all the targets from the side of the highway. Next, the moving vehicles are classified according to their type, based on which the traffic load is provided to the user in each highway lane. Due to the radial velocity, low Doppler frequency, and variability with time, some challenges will emerge, for which two solutions will be provided. This paper describes the speed of cars and the amount of congestion in each highway lane presented by two new methods. Finally, with the process of canceling clutters observed by radar and detecting possible errors, a highway statistic is presented as a table. [ABSTRACT FROM AUTHOR]

A multi-hypothesis marginal multi-target Bayes filter for heavy-tailed observation noise is proposed to track multiple targets in the presence of clutter, missed detection, and target appearing and disappearing. The proposed filter propagates the existence probabilities and probability density functions (PDFs) of targets in the filter recursion. It uses the Student's t distribution to model the heavy-tailed non-Gaussian observation noise, and employs the variational Bayes technique to acquire the approximate distributions of individual targets. K-best hypotheses, obtained by minimizing the negative log-generalized-likelihood ratio, are used to establish the existence probabilities and PDFs of targets in the filter recursion. Experimental results indicate that the proposed filter achieves better tracking performance than other filters. [ABSTRACT FROM AUTHOR]

The human visual attention system plays an important role in infrared target recognition because it can quickly and accurately recognize infrared small targets and has good scene adaptability. This paper proposes an infrared small target detection method based on an attention mechanism, which consists of three modules: a bottom-up passive attention module, a top-down active attention module, and decision feedback equalization. In the top-down active attention module, given the Gaussian characteristics of infrared small targets, the idea of combining knowledge-experience Gaussian shape features is applied to implement feature extraction, and quaternion cosine transform is performed to achieve multi-dimensional fusion of Gaussian shape features, thereby achieving complementary fusion of multi-dimensional feature information. In the bottom-up passive attention module, considering that the difference in contrast and motion between the target and the background can attract attention easily, an optimal fast local contrast algorithm and improved circular pipeline filtering are adopted to find candidate target regions. Meanwhile, the multi-scale Laplacian of the Gaussian filter is adopted to estimate the optimal size of the infrared small target. The fast local contrast algorithm based on box filter acceleration and structure optimization is employed to extract local contrast features, and candidate target regions can be obtained by using an adaptive threshold. Besides, the mean gray, target size, Gaussian consistency, and circular region constraint are used in pipeline filtering to extract motion regions, and the false-alarm rate is reduced effectively. Finally, decision feedback equalization is adopted to obtain real targets. Experiments are conducted on some real infrared images involving complex backgrounds with sea, sky, and ground clutters, and the experimental results indicate that the proposed method can achieve better detection performance than conventional baseline methods, such as RLCM, ILCM, PQFT, MPCM, and ADMD. Also, mathematical proofs are provided to validate the proposed method. [ABSTRACT FROM AUTHOR]

The Dual-Frequency Precipitation Radar (DPR), which consists of a Ku-band precipitation radar (KuPR) and a Ka-band precipitation radar (KaPR) on board the GPM Core Observatory, cannot observe precipitation at low altitudes near the ground contaminated by surface clutter. This near-surface region is called the blind zone. DPR estimates the clutter-free bottom (CFB), which is the lowest altitude not included in the blind zone, and estimates precipitation at altitudes higher than the CFB. High CFBs, which are common over mountainous areas, represent obstacles to detection of shallow precipitation and estimation of low-level enhanced precipitation. We compared KuPR data with rain gauge data from Da-Tun Mountain of northern Taiwan acquired from March 2014 to February 2020. A total of 12 cases were identified in which the KuPR missed some rainfall with intensity of .10 mm h21 that was observed by rain gauges. Comparison of KuPR profile and ground-based radar profile revealed that shallow precipitation in the KuPR blind zone was missed because the CFB was estimated to be higher than the lower bound of the range free from surface echoes. In the original operational algorithm, CFB was estimated using only the received power data of the KuPR. In this study, the CFB was identified by the sharp increase in the difference between the received powers of the KuPR and the KaPR at altitude affected by surface clutter. By lowering the CFB, the KuPR succeeded in detection and estimation of shallow precipitation. SIGNIFICANCE STATEMENT: The Dual-Frequency Precipitation Radar (DPR) on board the GPMCore Observatory cannot capture precipitation in the low-altitude region near the ground contaminated by surface clutter. This region is called the blind zone. The DPR estimates the clutter-free bottom (CFB), which is the lower bound of the range free from surface echoes, and uses data higher than CFB. DPR consists of a Ku-band precipitation radar (KuPR) and a Ka-band precipitation radar (KaPR). KuPR missed some shallow precipitation more than 10 mm h21 in the blind zone over Da-Tun Mountain of northern Taiwan because of misjudged CFB estimation. Using both the KuPR and the KaPR, we improved the CFB estimation algorithm, which lowered the CFB, narrowed the blind zone, and improved the capability to detect shallow precipitation. [ABSTRACT FROM AUTHOR]

In a radar detection system, multiple pulse (MP) transmission is used to improve detection performance compared to the single pulse case by integrating the echoes of pulses at reception. In this paper, we derive closed-form expressions of the probability of false alarm PFA of the cell averaging-constant false alarm rate, greatest-of CFAR, and smallest-of CFAR detectors considering a homogeneous gamma distributed radar clutter applied to the MP case. Expressions are given by analytical formulas for a positive real shape parameter which correspond to a real situation and are validated by comparing them in terms of the detection threshold calculated values T, to the results obtained by means of Monte Carlo simulations. [ABSTRACT FROM AUTHOR]

The Doppler spectra of sea echoes, which contain abundant information on floating scatterers, are important for exploring the characteristics of sea clutter. Using sea clutter data at low grazing angles observed by a coherent ultra-high frequency (UHF) radar located on Lingshan Island in the Yellow Sea, China, this study conducted detailed research on the characteristics of Doppler spectra with multiple ocean parameters, including grazing angle, significant wave height (SWH), and wave directions. The effect of sea echoes with different local normalized intensities on short-time Doppler spectra was further studied. The results indicate that with increasing sea states, the bimodal behavior of Doppler spectra, an evident phenomenon of Bragg scattering, gradually weakens. The frequency shifts of the mean spectra increased linearly with increasing SWH and wind speed, decreased linearly with increasing grazing angle, and decreased with the cosine value of the relative wave direction angles. In comparison, frequency shifts of the short-time spectra increased with increasing sea states and local echo intensities but fluctuated around a fixed value after reaching a certain extent. For spectral widths, the grazing angle is a significant influencing factor, with its broadening trend evident with a decrease in the grazing angle, whereas other ocean parameters, such as wave direction and wind direction, have no apparent influence. Considering the major contributions of the parameters, semi-empirical models for the mean spectral frequency shifts, mean spectral widths and short-time spectral frequency shifts were proposed. By verifying the measured data and predicted results, the models exhibited good prediction accuracy and applicability. The proposed inferences and models are helpful for understanding low grazing angle UHF-band sea clutter characteristics and improving target detection algorithms in offshore areas. These findings supplement previous studies on sea clutter Doppler spectra. [ABSTRACT FROM AUTHOR]

Copyright of Telecommunication Engineering is the property of Telecommunication 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.)

Manipulations of multiple carrier frequencies are especially important in a variety of fields like radar detection and wireless communications. In conventional radio-frequency architecture, the multi-frequency control is implemented by microwave circuits, which are hard to integrate with antenna apertures, thus bringing the problems of expensive system and high power consumption. Previous studies demonstrate the possibility to jointly control the multiple harmonics using space-time-coding digital metasurface, but suffer from the drawback of inherent harmonic entanglement. To overcome the difficulties, we propose a multi-partition asynchronous space-time-coding digital metasurface (ASTCM) to generate and manipulate multiple frequencies with more flexibility. We further establish an ASTCM-based transmitter to realize wireless communications with frequency-division multiplexing, where the metasurface is responsible for carrier-wave generations and signal modulations. The direct multi-frequency controls with ASTCM provides a new avenue to simplify the traditional wireless systems with reduced costs and low power consumption. The authors present a multi-partition asynchronous space-time-coding digital metasurface (ASTCM) to control multifrequency waves; they demonstrate its capabilities by sending eight pictures over distinct frequencies using this wireless transmitter. The proposed work shows potential in enhancing microwave systems with improved power efficiency and cost-effectiveness. [ABSTRACT FROM AUTHOR]

Inversion of atmospheric ducts is of great importance in the field of performance evaluation for radar and communication systems. Since the model parameters in machine learning play a crucial role in prediction performance, this paper develops a random forest (RF) model integrated with Bayesian optimization (BO) called BO-RF for atmospheric duct prediction, and the BO is adopted to determine appropriate model parameters during the training process. In addition, the K-fold cross-validation (CV) method is also incorporated into the model to obtain the best model partition and overcome the overfitting problem. To test the performance of the proposed model, the results obtained by the BO-RF are compared with other commonly used methods, such as classical RF, extreme gradient boosting (XGBoost) with/without BO, and K-nearest neighbor (KNN) with/without BO. Comparisons demonstrate that BO-RF has the best accuracy and anti-noise ability for the estimation of duct parameters. [ABSTRACT FROM AUTHOR]

Non‐sidelooking airborne radar encounters significant non‐stationary and heterogeneous clutter environments, resulting in a severe shortage of samples. Sparse recovery‐based space‐time adaptive processing (SR‐STAP) methods can achieve good clutter suppression performance with limited samples. Nonetheless, grid‐based SR‐STAP algorithms encounter off‐grid effects in non‐sidelooking arrays, which can severely degrade the clutter suppression performance. In this study, the authors propose a novel gridless SR‐STAP method in the continuous spatial‐temporal domain to address the issue of off‐grid effects. Inspired by the fact that sparse Bayesian learning (SBL) framework implicitly performs a structured covariance matrix estimation, the authors reparameterise its cost function to directly estimate the block‐Toeplitz structured matrix from the measurements in a gridless manner. Since the proposed cost function is non‐convex, we utilise a majorisation‐minimisation‐based iterative procedure to estimate the clutter covariance matrix. Finally, using the standard concept of semidefinite programming, the authors derive a convex gridless implementation of the SBL cost function for uniformly sampled radar systems. Extensive simulation experiments demonstrate the exceptional clutter suppression and target detection performance of the proposed algorithm. [ABSTRACT FROM AUTHOR]