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Radar imaging from unmanned aerial vehicles (UAVs) is a dynamic research topic attracting huge interest due to its practical fallouts. In this context, this article provides a comprehensive review of the current state of the art and challenges related to UAV-based ground-penetrating radar (GPR) imaging systems. First, a description of the available prototypes is provided in terms of radar technology, UAV platforms, and navigation control devices. Afterward, the paper addresses the main issues affecting the performance of UAV-based GPR imaging systems. such as the control of the UAV platform during the flight to collect high-quality data, the necessity to provide accurate platform position information in terms of probing wavelength, and the mitigation of clutter and other electromagnetic disturbances. A description of the major applicative areas for UAV GPR systems is reported with the aim to show their potential. Furthermore, the main signal-processing approaches currently adopted are detailed and two experimental tests are also reported to prove the actual imaging capabilities. Finally, open challenges and future perspectives regarding this promising technology are discussed. [ABSTRACT FROM AUTHOR]

To accurately reconstruct large-area three-dimensional current fields in coastal regions, simultaneous observations with ten coastal acoustic tomography (CAT) stations and two high-frequency radar (HFR) stations were performed in the Xiangshan Bay (XSB) on 4–5 December 2020. The section-averaged velocity that was observed by CAT and the radial velocity that was observed by HFR were, for the first time, synchronously assimilated into a three-dimensional barotropic ocean model. Compared with acoustic Doppler current profile data, the velocities of the model assimilating both CAT and HFR data had the highest accuracy according to root mean square differences (RMSDs), ranging from 0.05 to 0.08 m/s for all the vertical layers. For the models individually assimilating CAT and HFR, the values in the vertical layers ranged from 0.07 to 0.12 m/s and 0.08 to 0.13 m/s, respectively. A harmonic analysis of the model grid data showed that the spatial mean amplitudes of the M2, M4, and residual currents were 0.66, 0.14, and 0.09 m/s, respectively. Furthermore, the standing wave characteristics of the M2 current and M4 associated-oscillation in the inner XSB, mouth of the Xiangshan fjord, were better captured by the model assimilating both CAT and HFR. Our study demonstrates the advances in three-dimensional tidal current analysis using a model that assimilates both CAT and HFR data, especially in regions with complex coastal geography. [ABSTRACT FROM AUTHOR]

For quantitative precipitation estimation (QPE) based on polarimetric radar (PR) and rain gauges (RGs), the quality of the radar data is crucial for estimation accuracy. This paper proposes a combined radar quality index (CRQI) to represent the quality of the radar data used for QPE and an algorithm that uses CRQI to improve the QPE performance. Nine heavy rainfall events that occurred in Guangdong Province, China, were used to evaluate the QPE performance in five contrast tests. The QPE performance was evaluated in terms of the overall statistics, spatial distribution, near real-time statistics, and microphysics. CRQI was used to identify good-quality data pairs (i.e., PR-based QPE and RG observation) for correcting estimators (i.e., relationships between the rainfall rate and the PR parameters) in real-time. The PR-based QPE performance was improved because estimators were corrected according to variations in the drop size distribution, especially for data corresponding to 1.1 mm < average Dm < 1.4 mm, and 4 < average log10Nw < 4.5. Some underestimations caused by the beam broadening effect, excessive beam height, and partial beam blockages, which could not be mitigated by traditional algorithms, were significantly mitigated by the proposed algorithm using CRQI. The proposed algorithm reduced the root mean square error by 17.5% for all heavy rainfall events, which included three precipitation types: convective precipitation (very heavy rainfall), squall line (huge raindrops), and stratocumulus precipitation (small but dense raindrops). Although the best QPE performance was observed for stratocumulus precipitation, the biggest improvement in performance with the proposed algorithm was observed for the squall line. [ABSTRACT FROM AUTHOR]

Sanya incoherent scatter radar (SYISR) is a newly developed phased array incoherent scatter radar in the low latitudes of China located at Sanya (18.3°N, 109.6°E), Hainan Province. The main objective of SYISR is to observe the ionosphere. Given its frequency and power, it should have the capability to observe the troposphere. In this study, we show several tropospheric wind experiments that may indicate radar function expansion and capability verification, although observing the troposphere will not be an operation mode in the future. Reliable radar echoes were detected by SYISR up to 20 km with a turbulence scale of 0.35 m and a frequency of 430 MHz. Generally, both the geometric (GEO) method and the velocity azimuth display (VAD) method give similar wind profiles. Above 10 km, the discrepancy between the two methods becomes nonnegligible. For the same method, the discrepancy above 15–20 km among winds derived from different zenith angle measurements is nonnegligible. The VAD methods give more reasonable results at higher altitudes. The standard deviation of the difference (SYISR radar minus the reanalysis data ERA5) for zonal wind and meridional wind was 1.1 m/s and 0.78 m/s, respectively. During rainfall, we can distinguish the spectrum of rainfall and atmospheric turbulence from the power spectrum according to the spectral widths and Doppler frequency shifts. [ABSTRACT FROM AUTHOR]

This paper investigates the direction-of-arrival (DOA) estimation-based target localization problem using an array radar under complex multipath propagation scenarios. Prevalent methods may suffer from performance degradation due to the deterministic signal model mismatch, especially when the exact knowledge of a propagation environment is unavailable. To cope with this problem, we first establish an improved signal model of multipath propagation for low-angle target localization scenarios, where the dynamic nature of convoluted interferences induced by complex terrain reflections is taken into account. Subsequently, an iterative implementation-based target localization algorithm with the improved propagation model is proposed to eliminate the detrimental effect of coherent interferences on target localization performance. Compared to existing works, the proposed algorithm can maintain satisfactory estimation performance in terms of target location parameters, even in severe multipath interference conditions, where the decorrelation preprocessing and accurate knowledge about the multipath propagation environment are not required. Both simulation and experimental results demonstrate the effectiveness of the proposed propagation model and localization algorithm. [ABSTRACT FROM AUTHOR]

This paper studies the joint range and angle estimation of monostatic frequency diverse array multiple-input multiple-output (FDA-MIMO) radar and proposes a joint estimation algorithm. First, the transmit direction matrix is converted into real values by unitary transformation, and the Vandermonde-like matrix structure is used to construct an augmented output that doubles the aperture of the receive array. Then the augmented output is combined into a third-order tensor. Next, the factor matrices are initially estimated. Finally, the direction matrices are estimated utilizing parallel factor (PARAFAC) decomposition, and the range and angle are calculated by employing least square fitting. As contrasted with the classic PARAFAC method, the proposed method can estimate more targets and provide better estimation performance, and requires less computational complexity. The availability and excellence of the proposed method are reflected by numerical simulations and complexity analysis. [ABSTRACT FROM AUTHOR]

The observation of taking-off and landing behaviors of migratory insects is important for pest early monitoring and forecasting. Entomological radar, which can measure the ascent or descent rates remotely, has been proved to be the most effective way to observe the above behaviors. For the current entomological radars, the highest range resolution of 7.5 m and smallest blind range of 150 m make it difficult to distinguish individual insects in a swarm and observe the migratory behaviors at low altitudes. In this paper, based on the outfield data acquired from a high-resolution radar of 0.2 m, the spatial spacing distribution of migratory insects is presented for the first time and waveform design requirements are analyzed, proving the necessity of a higher resolution. Secondly, for the high-resolution and low blind range observation of insects, the stepped frequency train of LFM pulses (also called frequency-jumped burst, FJB) waveform is discussed as the optimal choice. In order to resolve the high grating-lobe problem in the FJB waveform with the low blind range, the precise spectrum model and its approximation of the LFM subpulse are first derived in detail. Then, according to the obtained spectrum characteristics, the high-resolution and low blind range FJB waveform design methods based on spectrum fluctuation period and Fresnel integral windowing are proposed to reduce the grating-lobe number to more than 50% and suppress the highest grating lobe level by at least 4 dB. Finally, based on the high-resolution and low blind range VLR adopting the proposed waveform, several typical taking-off and landing behavior observation results are presented, which proves that, in their migratory pattern, insects usually take off around sunset. [ABSTRACT FROM AUTHOR]

The vortex electromagnetic wave radar based on monostatic geometry can realize the imaging of a radar target in both range dimension and azimuth dimension by adjusting vortex electromagnetic wave modes, but it has no ability to distinguish the elevation of the radar target. To achieve three-dimensional imaging of the radar target, a three-dimensional imaging method of vortex electromagnetic wave based on bistatic geometry is proposed. The echo model of the target based on vortex electromagnetic wave in bistatic geometry is constructed firstly. The elevation resolution of the target is realized, and then the three-dimensional coordinate of the target is reconstructed. Moreover, the elevation resolution and the computational complexity of the proposed method are also analyzed. Simulation results demonstrate the effectiveness of the proposed method. The results also illustrate that the array element requirements and the computational complexity of presented algorithm are less than Multiple Signal Classification (MUSIC) algorithm. [ABSTRACT FROM AUTHOR]

Weather radar plays an important role in accurate weather monitoring and modern weather forecasting, as it can provide timely and refined weather forecasts for the public and for decision makers. Deep learning has been applied in radar nowcasting tasks and has exhibited a better performance than traditional radar echo extrapolation methods. However, current deep learning-based radar nowcasting models are found to suffer from a spatial “blurry” effect that can be attributed to a deficiency in spatial variability representation. This study proposes a Spatial Variability Representation Enhancement (SVRE) loss function and an effective nowcasting model, named the Attentional Generative Adversarial Network (AGAN), to alleviate this blurry effect by enhancing the spatial variability representation of radar nowcasting. An ablation experiment and a comparison experiment were implemented to assess the effect of the generative adversarial (GA) training strategy and the SVRE loss, as well as to compare the performance of the AGAN and SVRE loss function with the current advanced radar nowcasting models. The performances of the models were validated on the whole test set and inspected in two storm cases. The results showed that both the GA strategy and SVRE loss function could alleviate the blurry effect by enhancing the spatial variability representation, which helps the AGAN to achieve better nowcasting performance than the other competitor models. Our study provides a feasible solution for high-precision radar nowcasting applications.

A conventional way to monitor severe convective weather is using the composite reflectivity of radar as an indicator. For oceanic areas without radar deployment, reconstruction from satellite data is useful. However, those reconstruction models built on a land dataset are not directly applicable to the ocean due to different underlying surfaces. In this study, we built reconstruction models based on U-Net (named STR-UNet) for different underlying surfaces (land, coast, offshore, and sea), and evaluated their applicability to the ocean. Our results suggest that the comprehensive use of land, coast, and offshore datasets should be more suitable for reconstruction in the ocean than using the sea dataset. The comprehensive performances (in terms of RMSE, MAE, POD, CSI, FAR, and BIAS) of the Land-Model, Coast-Model, and Offshore-Model in the ocean are superior to those of the Sea-Model, e.g., with RMSE being 5.61, 6.08, 5.06, and 7.73 in the oceanic area (Region B), respectively. We then analyzed the importance of different types of features on different underlying surfaces for reconstruction by using interpretability methods combined with physical meaning. Overall, satellite cloud-related features are most important, followed by satellite water-related features and satellite temperature-related features. For the transition of the model from land to coast, then offshore, the importance of satellite water-related features gradually increases, while the importance of satellite cloud-related features and satellite temperature-related features gradually decreases. It is worth mentioning that in the offshore region, the importance of satellite water-related features slightly exceeds the importance of satellite cloud-related features. Finally, based on the performance of the case, the results show that the STR-UNet reconstruction models we established can accurately reconstruct the shape, location, intensity, and range of the convective center, achieving the goal of detecting severe convective weather where a radar is not present.

Assimilating the proper amount of water vapor into a numerical weather prediction (NWP) model is essential in accurately forecasting a heavy rainfall. Radar data assimilation can effectively initialize the three-dimensional structure, intensity, and movement of precipitation fields to an NWP at a high resolution (±250 m). However, the in-cloud water vapor amount estimated from radar reflectivity is empirical and assumes that the air is saturated when the reflectivity exceeds a certain threshold. Previous studies show that this assumption tends to overpredict the rainfall intensity in the early hours of the prediction. The purpose of this study is to reduce the initial value error associated with the amount of water vapor in radar reflectivity by introducing advanced remote sensing data. The ongoing research shows that errors can be largely solved by assimilating satellite all-sky radiances and global positioning system radio occultation (GPSRO) refractivity to enhance the moisture analysis during the cycling period. The impacts of assimilating moisture variables from satellite all-sky radiances and GPSRO refractivity in addition to hydrometeor variables from radar reflectivity generate proper amounts of moisture and hydrometeors at all levels of the initial state. Additionally, the assimilation of satellite atmospheric motion vectors (AMVs) improves wind information and the atmospheric dynamics driving the moisture field which, in turn, increase the accuracy of the moisture convergence and fluxes at the core of the convection. As a result, the accuracy of the timing and intensity of a heavy rainfall prediction is improved, and the hourly and accumulated forecast errors are reduced.

We processed five years of measurements (2018–2022) of a vertically pointing radar MIRA 35c at the Milešovka meteorological observatory with the aim of analyzing the cloud structure of thunderstorms and comparing differences in measured data for cases when lightning discharges were observed very close to the radar position, and for cases when lightning discharges were observed at a greater distance from the radar position. The MIRA 35c radar is a Doppler polarimetric radar working at 35 GHz (Ka-band) with a vertical resolution of 28.9 m and a time resolution of approximately 2 s. For the analysis, we considered radar data whose radar reflectivity was at least 10 dBZ at 5 km or higher above the radar to ensure that there was a cloud above the radar. We divided the radar data into “near” data (a lightning discharge was registered up to 1 km from the radar position) and “far” data (a lightning discharge was registered from 7.5 to 10 km from the radar position). We compared the following quantities: (i) Power in co-channel (pow), (ii) power in cross-channel (pow-cx), (iii) phase in co-channel (pha), (iv) phase in cross-channel (pha-cx), (v) equivalent radar reflectivity (Ze), (vi) Linear Depolarization Ratio (LDR), (vii) co-polar correlation coefficient (RHO), (viii) Doppler radial velocity (V), (ix) Doppler spectrum width (RMS), and (x) Differential phase (Phi). Pow, pow-cx, pha, pha-cx, and V are basic data measured by the radar, while Ze, LDR, RHO, RMS, and Phi are derived quantities. Our results showed that the characteristics of the compared radar quantities are clearly distinct for “near” dataset from “far” dataset. Furthermore, we found out that there is a clear evolution close to the time of discharges of the observed radar quantities in the “near” dataset, which is not that obvious in the “far” dataset.

Reflected waves have been frequently observed via weather radar, even during sunny, cloudless days. Additionally, it is recognized that turbulence and biological scatterers can also dominate the scattering process. In previous studies, echo returns of turbulence from clear-air Bragg scattering (CABS) have been used to detect the height of the convective boundary layer (CBL). However, in a dual-frequency antenna multiplexing radar system, through which the dual-wavelength ratio (DWR) identifies the CABS clearly, CABS can be unexpectedly observed not only at the edge of the CBL but also above the CBL, highlighting the need for an expanded set of causes of clear-air echoes. It was further identified that the negative second derivative of horizontal wind speed, which is measured by a coherent Doppler Lidar, is consistent with the variation of the CABS layers’ height above the CBL. These results emphasize the presence of physical processes leading to turbulence in the troposphere, with implications for Bragg scattering studies and the theory of turbulence in general. This study will help the ecology of research better understand the laws of biological activity.

A new method to classify electronic devices using a Frequency-Swept Harmonic Radar (FSHR) approach is proposed in this paper. The FSHR approach enables us to utilize the frequency diversity of the harmonic responses of the electronic circuits. Unlike previous studies, a frequency-swept signal with a constant power is transmitted to Electronic Circuits Under Test (ECUTs). The harmonic response to a frequency-swept transmitted signal is found to be distinguishable for different types of ECUTs. Statistical and Fourier features of the harmonic responses are derived for classification. Later, the harmonic characteristics of the ECUTs are depicted in 3D harmonic and feature spaces for classification. Three-dimensional harmonic and feature spaces are composed of the first three harmonics of the re-radiated signal and the statistical or Fourier features, respectively. We extensively evaluate the performance of our novel method through Monte Carlo simulations in the presence of noise. [ABSTRACT FROM AUTHOR]

In high-frequency surface wave radar (HFSWR) systems, clutter is a common phenomenon that causes objects to be submerged. Space-time adaptive processing (STAP), which uses two-dimensional data to increase the degrees of freedom, has recently become a crucial tool for clutter suppression in advanced HFSWR systems. However, in STAP, the pattern is distorted if a clutter component is contained in the main lobe, which leads to errors in estimating the target angle and Doppler frequency. To solve the main-lobe distortion problem, this study developed a clutter-suppression method based on beam reshaping (BR). In this method, clutter components were estimated and maximally suppressed in the side lobe while ensuring that the main lobe remained intact. The results of the proposed algorithm were evaluated by comparison with those of standard STAP and sparse-representation STAP (SR-STAP). Among the tested algorithms, the proposed BR algorithm had the best suppression performance and the most accurate main-lobe peak response, thereby preserving the target angle and Doppler frequency information. The BR algorithm can assist with target detection and tracking despite a background with ionospheric clutter. [ABSTRACT FROM AUTHOR]

For skywave over-the-horizon radar, beamforming techniques are often used to suppress airspace radio frequency interference because the high-frequency band is shared by many devices. To address the problems that the traditional beamforming method is not capable of recognizing the electromagnetic environment and that its performance is greatly affected by the accuracy of signal feature estimation, a cognitive beamforming method using range-Doppler (RD) map features for skywave radar is proposed. First, the RD map is weighted by a local attention model, and then, texture features are extracted as the inputs to a support vector machine. Finally, the support vector machine is used to predict the optimal diagonal loading factor. Simulation results show that the output signal-to-interference-plus-noise ratio is improved compared with previous methods. The proposed method is suitable for many kinds of common unsatisfactory scenarios, making it beneficial for engineering implementation. [ABSTRACT FROM AUTHOR]

Recently and successfully, the Chang'E-5 (CE-5) lander was launched on a mission to bring 1.731 kg of lunar soil back to Earth. To investigate various compositions of lunar regolith, we apply the Lunar Regolith Penetrating Radar (LRPR) as the same scientific payload installed on the CE-5 lander. Based on the high-accuracy imaging technique, we achieve subsurface imaging to process LRPR-measured data collected from the lunar-like exploration tests in our laboratory. In this paper, we propose the pseudo-spectral time-domain (PSTD) method as the underlying code to implement the reverse-time migration (RTM) method and restore the uncertain subsurface area. With the significant advantage of lower spatial sampling density, the PSTD-RTM method not only saves major computational resources, but also rapidly confirms the object prediction in the effective imaging area. To further analyze the LRPR measured data, we employ the spectrum window to remove high- and low-frequency noise, and thus improve imaging visibility to some extent. The imaging results in this paper can prove the reliability and efficiency of the PSTD-RTM method for subsurface discoveries in planetary exploration. [ABSTRACT FROM AUTHOR]

For anti-active-interference-oriented cognitive radar systems, the mismatch between the acquired and actual interference information may result in serious degradation of cognitive anti-active-interference performance. To yield more effective knowledge of the electromagnetic environment and eliminate the mismatch effect, the electromagnetic activity prediction technique, which deduces future electromagnetic behaviors based on current observations, has received increasing attention. However, high computational complexities limit the application of conventional electromagnetic activity prediction methods in dynamic active interference prediction with high real-time requirements. In this paper, the online sequential extreme learning machine (OS-ELM)-based method, which is dedicated to high-efficiency active interference activity prediction, is proposed. The advancement includes two aspects. First, benefiting from the single-hidden-layer network structure and recursive-formula-based output weight updating, the proposed OS-ELM-based frequency prediction (OS-ELM-FP) and OS-ELM-based angle prediction (OS-ELM-AP) models can predict the interference state and update the prediction model parameters with much higher computational efficiency. Second, the better generalization performance enables the proposed method to achieve smaller interference activity prediction errors compared with conventional methods. Numerical examples and prediction results based on measured jamming data demonstrate the advantages of the proposed method. [ABSTRACT FROM AUTHOR]

In the context of improving the dimensioning of observation and telecommunication, the characterization of the propagation canal is very important. Thus, accurate models of propagation phenomenona in their environment and above a rough surface (maritime or terrestrial) are of major interest for many applications (such as radar, communications, and teledetection). To provide solutions to this problem, in this paper, we propose a fast, memory-efficient, and accurate asymptotic method for 2D tropospheric propagation for a large band of frequency that accounts for relief, as well as ground composition and roughness. This latter is a two-way split-step wavelet scheme with an intrinsic stopping criterion. For overseas propagation, roughness effects are considered through a hybrid method. A complete theoretical comparison with SSF in terms of memory and time efficiency is proposed. Simulations in various environments (ground, sea, and snow), as well as different frequencies (UHF, S, and X-band) are performed to validate the method and highlight its advantages. To highlight the interest of the developed methodology, this latter is applied to different real-life applications, such as the prediction of radar coverage and the optimization of an antenna location. [ABSTRACT FROM AUTHOR]

The microphysical parameters of snowfall directly impact hydrological and atmospheric models. During the International Collaborative Experiment hosted at the Pyeongchang 2018 Olympic and Paralympic Winter Games (ICE-POP 2018), dual-frequency radar retrievals of particle size distribution (PSD) parameters were produced and assessed over complex terrain. The NASA Dual-frequency Dual-polarized Doppler Radar (D3R) and a collection of second-generation Particle Size and Velocity (PARSIVEL2) disdrometer observations were used to develop retrievals. The conventional look-up table method (LUT) and random forest method (RF) were applied to the disdrometer data to develop retrievals for the volume-weighted mean diameter (Dm), the shape factor (mu), the normalized intercept parameter (Nw), the ice water content (IWC), and the snowfall rate (S). Evaluations were performed between the D3R radar and disdrometer observations using these two methods. The mean errors of the retrievals based on the RF method were small compared with those of the LUT method. The results indicate that the RF method is a promising way of retrieving microphysical parameters, because this method does not require any assumptions about the PSD of snowfall. [ABSTRACT FROM AUTHOR]

Avian radar technologies have the potential to serve an important role in the quantification of bird movements and determining patterns of bird use in areas where human–wildlife conflicts might occur (e.g., airports, wind energy facilities). Ground-truthing studies are needed to help wildlife managers understand the biological meaning of radar information, as the capabilities and limitations of these technologies are relatively unknown. We conducted a study to evaluate the efficacy of three X-band marine radar sensors for tracking red-tailed hawks (Buteo jamaicensis) on or near the airfield at Chicago's O'Hare International Airport from September 2010 to May 2014. Specific information regarding red-tailed hawk locations derived from satellite telemetry was used to determine how frequently the three radar sensors provided corresponding tracks of these avian targets (i.e., synchronized monitoring). We examined various factors (e.g., bird altitude and distance to the radar) to determine if they had any influence on the frequency of synchronicity between satellite telemetry locations and radar tracks. We found evidence that as the distance between a hawk and the radars increased, the radars' ability to detect and track known avian targets decreased. Overall, the frequency of synchronization events for red-tailed hawks was low. Of the 1977 red-tailed hawk locations that should have been visible to the radar sensors, 51 of these bird movements were tracked by at least one of the radar sensors (2.6%). This study provides a new methodology for evaluating the performance of radar systems for tracking birds and determining what factors might influence overall performance. [ABSTRACT FROM AUTHOR]

We describe an extension of methods used for the derivation of wave information from compact high frequency radar systems and give examples of observations which are compared to measurements from wave buoys close to the radar coverage area. Previous methods were based on the application of the Pierson–Moskowitz wave energy model in the analysis of radar spectra. We describe the extension of the methods to apply a bimodal ocean wave spectral model which includes two different spectra covering different wave frequencies, e.g., from swell and wind-waves. Results are presented from data sets obtained from radar stations located in the USA and Portugal. [ABSTRACT FROM AUTHOR]

In this paper, in order to reduce the probability of the radar waveform intercepted by the passive detection system, the time-bandwidth product of the radar waveform is increased, and the detection probability of the radar waveform to the target is improved. This paper tackles the holographic RF stealth radar and proposes a joint coding waveform based on the linear frequency modulation (LFM) waveform. Joint coding uses complementary codes to perform phase-coding, and combines the codewords optimized by genetic algorithm in order to perform discrete frequency-coding waveform. The joint coding waveform model is theoretically analyzed, and the ambiguity function, pulse compression and target detection probability of the joint coding waveform are obtained by numerical simulation. In addition, the complexity of the algorithm and the low probability of intercept (LPI) characteristic of the joint coding waveform are analyzed. The results show that the joint coding waveform has an approximate "pushpin" ambiguity function, ultra-low sidelobe characteristics, better RF stealth and target detection performance. Finally, it has good application prospects in the current battlefield environment. [ABSTRACT FROM AUTHOR]

Since the height of sea detection radar antenna and ship targets is relatively low, it is generally believed that its over-the-horizon detection is mainly caused by the evaporation duct at sea. To fully understand the influence of atmospheric ducts on radar over-the-horizon detection, a shore-based navigation radar was used to carry out over-the-horizon detection experiments; radiosondes were used to measure the atmospheric profile and evaporation duct monitoring equipment was used to measure the evaporation duct. Based on experimental data and model simulation, a comparative analysis of a navigation radar's over-the-horizon detection, the evaporation duct, and the lower atmospheric duct is presented in this study. The results show that the atmospheric duct can affect the signal propagation of the navigation radar, thus resulting in over-the-horizon detection. The long-range over-the-horizon detection of the navigation radar is caused by the strong lower atmospheric duct, while the evaporation duct can generally only form weak over-the-horizon detection, which is different from the general cognition. [ABSTRACT FROM AUTHOR]

We develop, utilize, and validate techniques to produce a global data set of accurate coastal ocean surface vector winds. The dataset extends as near to the coast as 5 km and includes 10 years of SeaWinds on QuikSCAT ocean scatterometer data obtained from 1999 to 2009. We demonstrate improved retrievals over other large land-locked bodies of water as well, such as the Caspian Sea and the Great lakes. To determine the coastal winds we quantify the extent of land contamination in each scatterometer backscatter measurement and to the extent possible remove that contamination. After the measurements are thus corrected we retrieve winds with the corrected measurements using a previously published algorithm which has been extensively used for JPL scatterometer wind products. The coastal processing vastly increases the number of wind vector cells near coasts. We have ten times the number of wind vectors within 10 km of coast as without coastal processing, and over twice as many at 20 km from coast. These new wind vectors are high-quality, and have zero effect on non-coastal wind vectors. The effect of residual land contamination is quantified by comparing to buoys at varying distance from the coast and comparing coastal wind vector cells to oceanward neighbors. We show that the non-coastal QuikSCAT processing has very few good wind vectors nearer to the coast than about 22.5 km. In comparison to buoys, and oceanward neighbors, we find a small increase in speed errors of these new coastal wind vectors versus the performance of non-coastal QuikSCAT at 22.5 km, indicating the high-quality of these new coastal wind vectors. A quality control scheme is employed that flags regions where the coastal wind retrieval is poor due to the assumptions inherent in the technique being locally invalid. The coastal winds retrieved in this manner have been publicly distributed to the oceanography community and utilized in other published works. [ABSTRACT FROM AUTHOR]

Aiming at maximizing waveform diversity gain when designing a phase-coded multiple-input multiple-output (MIMO) radar waveform set, it is desirable that all waveforms are orthogonal to each other. Hence, the lowest possible peak cross-correlation ratio (PCCR) is expected. Meanwhile, low peak auto-correlation side-lobe ratio (PASR) is needed for good detection performance. However, it is difficult to obtain a closed form solution to the waveform set from the expected values of the PASR and PCCR. In this paper, the waveform set design problem is modeled as a multi-objective, NP-hard constrained optimization problem. Unlike conventional approaches that design the waveform set through optimizing a weighted sum objective function, the proposed optimization model evaluates the performance of multi-objective functions based on Pareto level and obtains a set of Pareto non-dominated solutions. That means that the MIMO radar system can trade off each objective function for different requirements. To solve this problem, this paper presents a multi-objective quantum genetic algorithm (MoQGA) based on the framework of quantum genetic algorithm. A new population update strategy for the MoQGA is designed based on the proposed model. Compared to the state-of-the-art methods, like BiST and Multi-CAN, the PASR and PCCR metrics of the waveform set are 0.95–3.91 dB lower with the parameters of the numerical simulation. The MoQGA is able to minimize PASR and PCCR of the MIMO radar waveform set simultaneously. [ABSTRACT FROM AUTHOR]

This study compares the hourly mesospheric horizontal winds observed by two collocated and independent low-latitude meteor radars operating at 37.5 MHz and 53.1 MHz in Kunming, China (25.6°N, 103.8°E). Upon analyzing simultaneously detected meteor echoes, we find a fixed angular deviation between the baselines of the two meteor radar antenna arrays within the east–north–up coordinate system. Then, we correct the deviation in the antenna azimuth direction using a novel method and recalculate the horizontal zonal and meridional winds. A comparison of the results before and after the correction shows strong consistency between the winds observed by both meteor radars within the entire detection altitude range. Furthermore, we summarize the performance of different techniques for measuring mesospheric winds. Ultimately, our statistical analysis approach allows the uncertainties associated with meteor radar wind observations to be more precisely estimated. [ABSTRACT FROM AUTHOR]

The infiltration of liquid water in a seasonal snowpack is a complex process that consists of two primary mechanisms: a semi-uniform melting front, or matrix flow, and heterogeneous preferential flow paths. Distinguishing between these two mechanisms is important for monitoring snow melt progression, which is relevant for hydrology and avalanche forecasting. It has been demonstrated that a single co-polarized upward-looking radar can be used to track matrix flow, whereas preferential flow paths have yet to be detected. Here, from within a controlled laboratory environment, a continuous polarimetric upward-looking C-band radar was used to monitor melting snow samples to determine if cross-polarized radar returns are sensitive to the presence and development of preferential flow paths. The experimental dataset consisted of six samples, for which the melting process was interrupted at increasing stages of preferential flow path development. Using a new serial-section hyperspectral imaging method, polarimetric radar returns were compared against the three-dimensional liquid water content distribution and preferential flow path morphology. It was observed that the cross-polarized signal increased by 13.1 dB across these experiments. This comparison showed that the metrics used to characterize the flow path morphology are related to the increase in cross-polarized radar returns spanning the six samples, indicating that the upward-looking polarimetric radar has potential to identify preferential flow paths. [ABSTRACT FROM AUTHOR]

Measuring migration flux with entomological radar is of great importance to assess the biomass of migratory insects and study the influence of insects on the ecosystem. However, the migration flux is measured with a large quantity of errors for the entomological radar without the ability of in-beam angle measurement, because the insect RCS is measured with the assumption that the insect flies over the beam center. When the insect does not pass through the beam center, the measured RCS is less than the true value. To improve the estimation accuracy of migration flux, a new estimation method of migration flux based on statistical hypothesis is proposed for radars working in the fixed-beam vertical-looking mode. This method avoids the RCS measurement error caused by the offset of the insect trajectory to the radar beam center by assuming that the insect flight trajectory is evenly distributed in the beam and calculating the average value of flux. This method is extended to be used in fixed-beam arbitrary pointing mode and a new proposed scanning mode. The effectiveness of the proposed method is verified by simulations and migration insect data measured by a radar. [ABSTRACT FROM AUTHOR]

We analyzed two convective storms that passed over or near the Milešovka meteorological observatory. The observatory is located at the top of a hill and has been recently equipped with a Doppler polarimetric X-band radar FURUNO WR2120 for cloud investigations. Our analysis was based mainly on Doppler polarimetric radar data measured in vertical cross-sections (RHI-Range-Height Indicator). Radar data was also used for classifying hydrometeors by a newly developed XCLASS (X-band radar CLASSification) algorithm. We also used rapid scan data measured by the geostationary satellite Meteosat Second Generation to validate radar measurements at the upper parts of storms. Although an attenuation correction was applied to the reflectivity and differential reflectivity measurements, the attenuation typical of X-band radars was noticeable. It was mainly manifested in the differential reflectivity, co-polar correlation coefficient and specific differential phase. Nevertheless, radar measurements can be used to analyze the internal cloud structure of severe convective storms. The XCLASS classification was developed by major innovation of a previously published algorithm. The XCLASS algorithm identifies seven types of hydrometeors: light rain, rain, wet snow, dry snow, ice, graupel, and hail. It uses measured horizontal and vertical radar reflectivity, specific differential phase, co-polar correlation coefficient, and temperature, and applies fuzzy logic to determine the type of hydrometeor. The new algorithm practically eliminates unrealistic results around and below the melting layer provided by the original algorithm. It identifies wet snow in more cases, and areas with individual hydrometeors have more realistic shapes compared to the original algorithm. The XCLASS algorithm shows reasonable results for the classification of hydrometeors and can be used to study the structure of convective storms. [ABSTRACT FROM AUTHOR]

In this study, a multi-station and multi-instrument system, organized and proposed for ionospheric scintillation and equatorial spread-F (ESF) specification and their associated motions in the Taiwan–Philippines sector, is outlined. The issues related to the scintillation and ESF event observed on 26 October 2021, at magnetic quiet conditions are presented and discussed. We first indicate the existence of a plasma bubble in the Taiwan–Philippines sector by using the FormoSat-7/Constellation Observing System for Meteorology, Ionosphere, and Climate-2 (FS7/COSMIC2) GPS/GLONASS radio occultation observations. We verify the latitudinal extent of the tracked plasma bubble using the recorded ionograms from the Vertical Incidence Pulsed Ionospheric Radar located at Hualien, Taiwan. We further discuss the spatial and temporal variabilities of two-dimensional vertical scintillation index VS4 maps based on the simultaneous GPS L1-band signal measurements from 133 ground-based receivers located in Taiwan and the surrounding islands. We also operate two high-sampling, software-defined GPS receivers and characterize the targeted plasma irregularities by carrying out spectrum analyses of the received signal. As a result, the derived plasma irregularities moved eastward and northward. Furthermore, the smaller the irregularity scale, the higher the spectral index and the stronger the scintillation intensity were at lower latitudes on the aimed irregularity feature. [ABSTRACT FROM AUTHOR]

Prior studies have highlighted the importance of calibrating receiver antennas in target direction-of-arrival (DOA) estimation and surface current measurement for high-frequency (HF) radar systems. It is worth noting that the calibration contributes to the performance of both shore-based HF radar and platform-mounted HF radar. Compared with shore-based HF radar, the influence of six-degrees-of-freedom (six-DOF) platform motion should be considered in the calibration of platform-mounted HF radar. This paper initially describes a calibration scheme that receives phased-array antennas for an anchored platform-mounted HF radar incorporating a model of free rotation, which is called yaw rotation and dominates the six-DOF platform motion in this study. In the presence of yaw rotation, the amplitude and phase of the source calibration signal from the other shore-based radar sites reveal the directional sensitivity of the receiver phased-array antennas. The calibration of receiver phased-array antennas is composed of channel calibration (linking cables and receiver hardware calibration) and antenna pattern calibration. The antenna pattern at each bearing can be represented by the Fourier series. The estimation of channel calibration and antenna pattern calibration depends on an overdetermined HF radar system consisting of observed values and theoretical constraints, so the least-squares fits of the channel calibration coefficients and antenna pattern calibration coefficients are obtained. The experimental results show that the target DOA estimation and surface current measurement can be improved if the phased-array platform-mounted HF radar system is calibrated. [ABSTRACT FROM AUTHOR]

This study investigates the variations in flow patterns in the northern Taiwan Strait in summer using high-frequency (HF) radar measurements, satellite-tracked drifter trajectories and numerical models. There is an obvious interaction between intra-diurnal tides and ocean currents in northwestern Taiwan. When the tide changes between high tide and low tide, the change in direction of the nearshore flow occurs before the change in the offshore flow. Drifter trajectories show that there are three different drifting paths in the Taiwan Strait in summer. One path is along the west coast of Taiwan from the southwest coast to the northeast coast. Another path is the same as the first one but leads northward to the East China Sea instead of eastward to the northeast coast of Taiwan. The other path exists along the west coast of Taiwan, some distance out, after being deflected by the bottom ridge. The regional ocean modeling system model was used in this study to clarify the influencing factors that lead to these three paths. The results of multiple simulations and HF radar data indicate that the bifurcation of the first two drift paths in northwestern Taiwan is caused by ebb and flood tide transitions. The different routes of the latter two paths are due to the significant speed difference between the nearshore current and the offshore current approximately 45 km from the coast. [ABSTRACT FROM AUTHOR]

The least absolute shrinkage and selection operator (LASSO) algorithm is a promising method for sparse source location in time–division multiplexing (TDM) multiple–input, multiple–output (MIMO) radar systems, with notable performance gains in regard to resolution enhancement and side lobe suppression. However, the current batch LASSO algorithm suffers from high–computational complexity when dealing with massive TDM–MIMO observations, due to high–dimensional matrix operations and the large number of iterations. In this paper, an online LASSO method is proposed for efficient direction–of–arrival (DOA) estimation of the TDM–MIMO radar based on the receiving features of the sub–aperture data blocks. This method recursively refines the location parameters for each receive (RX) block observation that becomes available sequentially in time. Compared with the conventional batch LASSO method, the proposed online DOA method makes full use of the TDM–MIMO reception time to improve the real–time performance. Additionally, it allows for much less iterations, avoiding high–dimensional matrix operations, allowing the computational complexity to be reduced from O K 3 to O K 2 . Simulated and real–data results demonstrate the superiority and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The increasing availability of multiplatform, multiband, very-high-resolution (VHR) satellite synthetic aperture radar (SAR) data has attracted the attention of a growing number of scientists and archeologists. In particular, over the last two decades, archeological research has benefited from SAR development mainly due to its unique ability to acquire scenes both at night and during the day under all weather conditions, its penetration capability, and the provided polarimetric and interferometric information. This paper explored the potential of a novel method (nonlocal (NL)-SAR) using TerraSAR-X (TSX) and Constellation of Small Satellites for Mediterranean Basin Observation (COSMO)-SkyMed (CSK) data to detect buried archeological remains in steep, rugged terrain. In this investigation, two test sites were selected in southern Tunisia, home to some of the most valuable and well-preserved limes from the Roman Empire. To enhance the subtle signals linked to archeological features, the speckle noise introduced into SAR data by the environment and SAR system must be mitigated. Accordingly, the NL-SAR method was applied to SAR data pertaining to these two significant test sites. Overall, the investigation (i) revealed a fortified settlement from the Roman Empire and (ii) identified an unknown urban area abandoned during this period via a field survey, thus successfully confirming the capability of SAR data to reveal unknown, concealed archeological sites, even in areas with a complex topography. [ABSTRACT FROM AUTHOR]

HF radars are now an important part of operational coastal observing systems where they are used primarily for measuring surface currents. Their use for wave and wind direction measurement has also been demonstrated. These measurements are based on physical models of radar backscatter from the ocean surface described in terms of its ocean wave directional spectrum and the influence thereon of the surface current. Although this spectrum contains information about the local wind that is generating the wind sea part of the spectrum, it also includes spectral components propagating into the local area having been generated by winds away from the area i.e., swell. In addition, the relationship between the local wind sea and wind speed depends on fetch and duration. Thus, finding a physical model to extract wind speed from the radar signal is not straightforward. In this paper, methods that have been proposed to date will be briefly reviewed and an alternative approach is developed using machine learning methods. These have been applied to three different data sets using different radar systems in different locations. The results presented here are encouraging and proposals for further development are outlined. [ABSTRACT FROM AUTHOR]

Previous ground-based, radar lunar imaging experiments have usually employed the Range-Doppler (RD) algorithm. This algorithm performs in the frequency domain and has high computational efficiency. However, in the case of a long coherent integration time, the defocus phenomenon will appear, and the image will be smeared. This study proposes the use of the back projection (BP) algorithm to obtain focused lunar images to solve this problem. The BP algorithm is a time-domain algorithm which is frequently employed in synthetic aperture radar (SAR) imaging and can theoretically achieve the focused imaging of each pixel in an arbitrarily long coherent integration time. However, the largest drawback of this algorithm is its high computational complexity. Therefore, this study only applies this method to map local regions of the moon. We select Sanya incoherent scatter radar (SYISR) as the transmitting and receiving device and utilize the linear frequency modulation chirp pulse to transmit right-hand, circularly polarized electromagnetic waves and to receive left-hand, circularly polarized echoes. RD and BP algorithms are simultaneously adopted to image the Pythagoras crater region, and a contrastive analysis is performed. The results show that the BP algorithm can be well applied to a ground-based, radar lunar imaging experiment and that it has a better focusing performance, but the effect is not as obvious as expected. Thus, the processing method needs to be further improved. In addition, the computational efficiency of BP is very low, and certain fast algorithms need to be applied to improve it. [ABSTRACT FROM AUTHOR]

Interferometric radar is a hot research topic in manmade target displacement measuring applications, as it features high precision, a large operation range, and a remote multiple point measuring ability. Most one-dimensional interferometric radars use single-input single-output (SISO) radar architecture to achieve a high repetition measuring rate of more than 200 Hz; however, it cannot resolve multiple targets with the same radial range but different azimuth angles. This paper presents a multiple-input multiple-output (MIMO) radar that adopts a limited number of antennas (usually tens) to simultaneously improve azimuth resolution and achieve a high repetition measuring rate. A MUSICAPES algorithm is proposed, which is cascades the multiple signal classification (MUSIC) algorithm and the amplitude and phase estimation (APES) filter. The MUSIC algorithm is used to further improve the angular resolution of the small array. The APES is used to precisely recover the phases of the multiple close targets by suppressing their mutual interferences. Simulations and experiments with a millimeter-wave radar validate the performance of the proposed method. [ABSTRACT FROM AUTHOR]

In near-field remote sensing, noncontact methods (radars) that measure stage and surface water velocity have the potential to supplement traditional bridge scour monitoring tools because they are safer to access and are less likely to be damaged compared with in-stream sensors. The objective of this study was to evaluate the use of radars for monitoring the hydraulic conditions that contribute to bridge–pier scour in gravel-bed channels. Measurements collected with a radar were also leveraged along with minimal field measurements to evaluate whether time-integrated stream power per unit area (Ω) was correlated with observed scour depth at a scour-critical bridge in Colorado. The results of this study showed that (1) there was close agreement between radar-based and U.S. Geological Survey streamgage-based measurements of stage and discharge, indicating that radars may be viable tools for monitoring flow conditions that lead to bridge pier scour; (2) Ω and pier scour depth were correlated, indicating that radar-derived Ω measurements may be used to estimate scour depth in real time and predict scour depth based on the measured trajectory of Ω. The approach presented in this study is intended to supplement, rather than replace, existing high-fidelity scour monitoring techniques and provide data quickly in information-poor areas. [ABSTRACT FROM AUTHOR]

Globally consistent long-term radar measurements are imperative for understanding the global climatology and potential trends of convection. This study investigates the consistency of vertical profiles of reflectivity (VPR) and 20-dBZ echo-top height (Topht20) between the two precipitation radars onboard the Tropical Rainfall Measuring Mission (TRMM) and Global Precipitation Measurement (GPM) satellites. Results show that VPR coincidently observed by the TRMM's and GPM's Ku-band radar agree well for both convective and stratiform precipitation, although certain discrepancies exist in the VPR of weak convection. Topht20s of the TRMM and GPM are consistent either for coincident events, or latitudinal mean during the 7-month common period, all with biases within the radar range resolution (0.1–0.2 km). The largest difference in the Topht20 between the TRMM's and GPM's Ku-band radar occurs in shallow precipitation. Possible reasons for this discrepancy are discussed, including sidelobe clutter, beam-mismatch, non-uniform beam filling, and insufficient sampling. Finally, a 23-year (1998–2020) climatology of Topht20 has been constructed from the two spaceborne radars, and the global mean Topht20 time series shows no significant trend in convective depth during the last two decades. [ABSTRACT FROM AUTHOR]

There is no doubt that chaotic systems are still attractive issues in various radar applications and communication systems. In this paper, we present a new 0.3 GHz mono-static microwave chaotic radar. It includes a chaotic system based on a time-delay to generate and process frequency modulated (FM) waveforms. Such a radar is designed to extract high-resolution information from the targets. To generate a continuous FM signal, the chaotic signal is first modulated using the voltage control oscillator (VCO). Next, the correct value for the loop gain (G) is carefully set when utilizing the Phase-Locked Loop (PLL) at the receiver, so that the instantaneous frequency that reflects a chaotic state variable can be reliably recovered. In this system, the PLL synchronization and radar correlation are enough to recover the echo signal and detect the target. The finding indicates that the system can be implemented with no need to use the complete self-synchronization or complex projective synchronization schemes as compared to the existing chaotic radar systems. The simulation results show that the short-time cross-correlation of the transmitted and reconstructed waveforms is good and satisfactory to detect the target under various signal-to-noise ratio (SNR) levels and with less complexity in the design. [ABSTRACT FROM AUTHOR]

To solve the difficulty associated with radar signal classification in the case of few-shot signals, we propose an adaptive focus loss algorithm based on transfer learning. Firstly, we trained a one-dimensional convolutional neural network (CNN) with radar signals of three intra-pulse modulation types in the source domain, which were effortlessly obtained and had sufficient samples. Then, we transferred the knowledge obtained by the convolutional layer to nine types of few-shot complex intra-pulse modulation classification tasks in the target domain. We propose an adaptive focal loss function based on the focal loss function, which can estimate the parameters based on the ratio of hard samples to easy samples in the data set. Compared with other existing algorithms, our proposed algorithm makes good use of transfer learning to transfer the acquired prior knowledge to new domains, allowing the CNN model to converge quickly and achieve good recognition performance in case of insufficient samples. The improvement based on the focal loss function allows the model to focus on the hard samples while estimating the focusing parameter adaptively instead of tediously repeating experiments. The experimental results show that the proposed algorithm had the best recognition rate at different sample sizes with an average recognition rate improvement of 4.8%, and the average recognition rate was better than 90% for different signal-to-noise ratios (SNRs). In addition, upon comparing the training processes of different models, the proposed method could converge with the least number of generations and the shortest time under the same experimental conditions. [ABSTRACT FROM AUTHOR]

The interferometric imaging radar altimeter (InIRA) adopts a short baseline along with small incidence angles to acquire interferometric signals from the sea surface with high accuracy, thus the wide-swath sea surface height (SSH) and backscattering coefficient ( σ 0 ) can be obtained simultaneously. This work presents an approach to jointly retrieve the wind speed and significant wave height (SWH) for the Chinese Tiangong-2 interferometric imaging radar altimeter (TG2-InIRA). This approach utilizes a multilayer perceptron (MLP) joint retrieval model based on σ 0 and SSH data. By comparing with the European Center for Medium-Range Weather Forecasts (ECMWF) reanalysis data, the root mean square errors (RMSEs) of the retrieved wind speed and the SWH are 1.27 m/s and 0.36 m, respectively. Based on the retrieved SWH, two enhanced wind speed retrieval models are developed for high sea states and low sea states, respectively. The results show that the RMSE of the retrieved wind speed is 1.12 m/s when the SWHs < 4 m; the RMSE is 0.73 m/s when the SWHs ≥ 4 m. Similarly, two enhanced SWH retrieval models for relatively larger and relatively smaller wind speed regions are developed based on the retrieved wind speed with corresponding RMSEs of 0.19 m and 0.16 m, respectively. The comparison between the retrieved results and the buoy data shows that they are highly consistent. The results show that the additional information of SWH can be used to improve the accuracy of wind speed retrieval at small incidence angles, and also the additional information of wind speed can be used to improve the SWH retrieval. The stronger the correlation between wind speed and SWH, the greater the improvement of the retrieved results. The proposed method can achieve joint retrieval of wind speed and SWH accurately, which complements the existing wind speed and SWH retrieval methods for InIRA. [ABSTRACT FROM AUTHOR]

Range-ambiguous clutter is an inevitable issue for airborne forward-looking array radars, especially with the high pulse repetition frequency (PRF). In this paper, a method to suppress the range-ambiguous clutter is proposed in an FDA-MIMO radar with a forward-looking planar array. Compressed sensing FDA technology is used to suppress the range-ambiguous clutter and the forward-looking non-uniformity short-range clutter of radar. Specifically, first, the range ambiguous clutter in different regions is separated by the characteristics of the planar array radar elevation dimension and FDA radar range coupling. Meanwhile, regarding the issue of the FDA radar main lobe moving between coherent pulses, a main lobe correction (MLC) algorithm proposes a solution for the issue, where the FDA radar cannot coherently accumulate signals in the case of non-full angle illumination. Finally, compressed sensing technology and elevation dimension filtering are utilized to suppress the range ambiguous clutter at the receiver, with the approach alleviating the range dependence of clutter in the observation region. A small number of clutter snapshots can obtain an approximately ideal clutter covariance matrix through compressed sensing sparse recovery. The method not only reduces the number of training samples, but also overcomes the problem of clutter non-uniformity in the forward-looking array. Therefore, the clutter suppression problems faced by the high repetition frequency airborne radar forward-looking array structure are solved. At the analysis stage, a comparison among the conventional MIMO and FDA methods is carried on by analyzing the improvement factor (IF) curves. Numerical results verify the effectiveness of the proposed method in range-ambiguous clutter suppression. [ABSTRACT FROM AUTHOR]

This paper discusses the issue of low-angle estimation in multiple-input, multiple-output (MIMO) radar. Due to the low amount of data transmission, storage, and computation required for beamspace super-resolution algorithms in low-angle estimation, the method has gained considerable interest in recent years. This paper develops a beamspace scene classification (BSC) algorithm to enhance the performance of low-angle estimation in MIMO radar. The proposed BSC algorithm solves an initial angle estimate and the multipath coefficient estimate using the 3D beamspace data, and further constructs the beamspace data required for transmitting and receiving sides to reduce the data dimensions. It is additionally used to provide three estimation schemes with closed-form solutions for three multipath scenes (obtaining two estimations for transmitting and receiving sides). Finally, it fuses the estimates of the two sides by the minimum variance criterion. As a result, the proposed method achieves high estimation accuracy while requiring few processing resources. Moreover, the computational complexity of the proposed algorithm is examined in this study, with the results demonstrating that the proposed method is superior for engineering applications. [ABSTRACT FROM AUTHOR]

Due to the small size and various types of foreign object debris (FOD), radar detection of FOD on airport runways is a great challenge, and there are often a large number of false alarms in the detection results. Arc-scanning synthetic aperture radar (AS-SAR) is an emerging method for detecting FOD targets, which achieves omnidirectional coverage with a very high azimuth resolution. However, this method faces a similar challenge. A direct way to reduce false alarms is to increase the detection threshold based on enhancing the target signal-to-noise ratio (SNR), and in this paper, the coherent accumulation of multiple images is used to improve the target SNR. The stable phase is also an important feature of the target distinguishing background. Therefore, it is important to maintain the stability of the target phase. Aiming at the systematic phase drift (SPD) caused by atmospheric disturbance and system hardware, a spatial and temporal model is established, a corresponding correction approach is proposed, and the performance of the correction approach is validated by field experiments. [ABSTRACT FROM AUTHOR]

The development of Intelligent Transportation Systems (ITS) puts forward higher requirements for millimeter-wave radar surveillance in the traffic environment, such as lower time delay, higher sensitivity, and better multi-target detection capability. The Constant False Alarm Rate (CFAR) detector plays a vital role in the adaptive target detection of the radar. Still, traditional CFAR detection algorithms use a sliding window to find the target limit radar detection speed and efficiency. In such cases, we propose and discuss a CFAR detection method, which transforms the Monte Carlo simulation principle into randomly sampling instantaneous Range–Doppler Matrix (RDM) data, to improve the detection ability of radar for moving targets such as pedestrians and vehicles in the traffic environment. Compared with conventional methods, simulation and real experiments show that the method breaks through the reference window limitation and has higher detection sensitivity, higher detection accuracy, and lower detection delay. We hope to promote the detection application of millimeter-wave radar in road traffic scenes. [ABSTRACT FROM AUTHOR]

The tone reservation (TR) approach is mainly adopted to reduce the peak-to-mean envelope power ratio (PMEPR) of orthogonal frequency division multiplexing (OFDM) waveform with low TR ratio (TRR) in classic 4G communication systems. However, for the OFDM integrated radar and communication waveform, high TRR is necessary to simultaneously maintain the radar detection performance as well as communication performance. For cases with high TRR, the traditional waveform optimization algorithms have low execution efficiency and a poor PMEPR convergence level, and thus a new algorithm is needed. This paper proposes a new PMEPR optimization algorithm based on conjugate gradient. Firstly, by introducing the concept of L p -norm, the PMEPR of the OFDM waveform is accurately remodeled as the objective function of the waveform optimization problem. Secondly, the conjugate gradient of the objective function is analytically derived to form the basis of the efficient PMEPR optimization. Finally, a PMEPR optimization algorithm based on the Polak–Ribière–Polyak (PRP) conjugate gradient is proposed. The simulation results verified the proposed algorithm in terms of optimization efficiency, as well as convergence level, and the initial experimental results suggested the practicality of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Intense atmospheric disturbances, which impact directly on the sea surface causing a significant increase in wave height and sometimes strong storm surges, have become increasingly frequent in recent years in the Mediterranean Sea, producing extreme concern in highly populated coastal areas, such as the Gulf of Naples (Western Mediterranean Sea, Central Tyrrhenian Sea). In this work, fifty-six months of wave parameters retrieved by an HF radar network are integrated with numerical outputs to analyze the seasonality of extreme events in the study area and to investigate the performance of HF radars while increasing their distances from the coast. The model employed is the MWM (Mediterranean Wind-Wave Model), providing a wind-wave dataset based on numerical models (the hindcast approach) and implemented in the study area with a 0.03° spatial resolution. The integration and comparison with the MWM dataset, carried out using wave parameters and spectral information, allowed us to analyze the availability and accuracy of HF sampling during the investigated period. The statistical comparisons highlight agreement between the model and the HF radars during episodes of sea storms. The results confirm the potential of HF radar systems as long-term monitoring observation platforms, and allow us to give further indications on the seasonality of sea storms under different meteorological conditions and on their energy content in semi-enclosed coastal areas, such as the Gulf of Naples. [ABSTRACT FROM AUTHOR]

Radar quantitative precipitation estimation (QPE) is one of the primary tasks of weather radars. The QPE quality was substantially improved after polarimetric upgrade of the radars. This study provides an overview of existing polarimetric methodologies for rain and snow estimation and their operational implementation. The variability of drop size distributions (DSDs) is a primary factor affecting the quality of rainfall estimation and its impact on the performance of various radar rainfall relations at S, C, and X microwave frequency bands is one of the focuses of this review. The radar rainfall estimation algorithms based on the use of specific attenuation A and specific differential phase KDP are the most efficient. Their brief description is presented and possible ways for their further optimization are discussed. Polarimetric techniques for the vertical profile of reflectivity (VPR) correction at longer distances from the radar are also summarized. Radar quantification of snow is particularly challenging and it is demonstrated that polarimetric methods for snow measurements show good promise. Finally, the article presents a summary of the latest operational radar QPE products available in the US by integration of the information from the WSR-88D radars via the Multi-Radar Multi-Sensor (MRMS) platform. [ABSTRACT FROM AUTHOR]