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The coherent mono-pulse instrumentation radar changes the pulse repetition frequency to resolve range and velocity ambiguities and improve detection rates. Owing to this change in the pulse repetition frequency, the time–Doppler map of the coherent mono-pulse instrumentation radar cannot be obtained from methods used by existing Doppler radars, such as FFT or SFTF. In this study, we propose a method to obtain a time–Doppler map by considering the pulse repetition frequency change and velocity ambiguity of a coherent mono-pulse instrumentation radar. The simulation results confirm that the movement of the target can be visualized on a time–Doppler map. [ABSTRACT FROM AUTHOR]

Tornadoes, as highly destructive weather events, require accurate detection for effective decision‐making. Traditional radar‐based tornado detection algorithms (TDA) face challenges with limited tornado feature extraction capabilities, leading to high false alarm rates and low detection probabilities. This study introduces the Multi‐Task Identification Network (MTI‐Net), leveraging Doppler radar data to enhance tornado recognition. MTI‐Net integrates tornado detection and estimation tasks to acquire comprehensive spatial and locational information. As part of MTI‐Net, we introduce a novel backbone network of Multi‐Head Convolutional Block (MHCB), which incorporates Spatial and Channel Attention Units (SAU and CAU). SAU optimizes local tornado feature extraction, while CAU reduces false alarms by enhancing dependencies among input variables. Experiments demonstrate the superiority of MTI‐Net over TDA, with a decrease in false alarm rates from 0.94 to 0.46 and an increase in hit rates from 0.23 to 0.81, highlighting the effectiveness of MTI‐Net in handling small‐scale tornado events. Plain Language Summary: Tornadoes, highly destructive small‐scale weather phenomena, demand accurate detection for informed decision‐making. Although meteorological radars are commonly utilized for tornado identification, current methods often suffer from false alarms or missed detections due to radar noise. In this study, we introduce the multi‐task learning‐based identification network (MTI‐Net), which not only enables tornado detection but also estimates tornado counts within radar data. We integrate Convolutional Neural Networks (CNNs) with Transformer techniques to enhance the model's ability to capture tornado information. CNNs detect local details using filters, while Transformers manage global connections through attention mechanisms. A series of experiments demonstrate significant improvements in tornado detection with MTI‐Net compared to traditional methods. Key Points: A tornado dataset with detailed radar features was created over China from 2017 to 2023Multi‐task learning was designed to simultaneously infer tornado detection and tornado number estimationIntegration of spatial and channel attention units can better extract tornado features from radar data [ABSTRACT FROM AUTHOR]

Unmanned surface vehicles (USVs) offer significant value through their capability to undertake hazardous and time-consuming missions across water surfaces. Recently, as the application of USVs has been extended to nearshore waterways, object tracking is vital to the safe navigation of USVs in offshore scenes. However, existing tracking systems for USVs are mainly based on cameras or LiDAR sensors, which suffer from drawbacks such as lack of depth perception or high deployment costs. In contrast, millimeter-wave (MMW) radar offers advantages in terms of low cost and robustness in all weather and lighting conditions. In this work, to construct a robust and low-cost tracking system for USVs in complex offshore scenes, we propose a novel MMW radar-based object tracking method (ROTracker). The proposed ROTracker combines the physical properties of MMW radar with traditional tracking systems. Specifically, we introduce the radar Doppler velocity and a designed motion discriminator to improve the robustness of the tracking system toward low-speed targets. Moreover, we conducted real-world experiments to validate the efficacy of the proposed ROTracker. Compared to other baseline methods, ROTracker achieves excellent multiple object tracking accuracy in terms of 91.9% in our collected dataset. The experimental results demonstrated that the proposed ROTracker has significant application potential in both accuracy and efficiency for USVs, addressing the challenges posed by complex nearshore environments. [ABSTRACT FROM AUTHOR]

The Eastern Pacific Cloud Aerosol Precipitation Experiment (EPCAPE) was a year-round campaign conducted by the US Department of Energy at the Scripps Institution of Oceanography in La Jolla, CA, USA, with a focus on characterizing atmospheric processes at a coastal location. The ground-based prototype of a new Ka-, W-, and G-band (35.75, 94.88, and 238.8 GHz) profiling atmospheric radar, named CloudCube, which was developed at the Jet Propulsion Laboratory, took part in the experiment during 6 weeks in March and April 2023. This article describes the unique data sets that were obtained during the field campaign from a variety of marine clouds and light precipitation. These are, to the best of the authors' knowledge, the first observations of atmospheric clouds using simultaneous multifrequency measurements including 238.8 GHz. These data sets therefore provide an exceptional opportunity to study and analyze hydrometeors with diameters in the millimeter- and submillimeter size range that can be used to better understand cloud and precipitation structure, formation, and evolution. The data sets referenced in this article are intended to provide a complete, extensive, and high-quality collection of G-band data in the form of Doppler spectra and Doppler moments. In addition, Ka-band and W-band reflectivity and Ka-, W-, and G-band reflectivity ratio profiles are included for several cases of interest on 6 different days. The data sets can be found at https://doi.org/10.5281/zenodo.10076227 (Socuellamos et al., 2024). [ABSTRACT FROM AUTHOR]

Interrupted sampling repeater jamming (ISRJ) is an advanced form of coherent jamming, and the suppression of this jamming has become a critical problem for modern radar electronic countermeasures. In this paper, we propose a countermeasure based on the linear interpulse frequencycoding linear frequency modulation (LIFC-LFM) signal. The LIFC refers to the linear encoding of the frequency of each pulse transmitted by the radar system, which can change the distribution of the false targets formed by ISRJ in the range-Doppler (RD) spectrum. In this context, we design the frequency coding value to effectively separate the true and false targets in the RD spectrum. Furthermore, we propose a fast-time phase compensation method to separate the true and false targets in the Doppler dimension to facilitate false target suppression. Finally, ISRJ can be suppressed by oblique projection processing. Simulation examples demonstrate that the proposed method has an excellent and robust ISRJ suppression effect for direct forwarding ISRJ, repeated forwarding ISRJ, and frequency shifting ISRJ. Meanwhile, the signal-to-noise ratio loss caused by the jamming suppression is small. [ABSTRACT FROM AUTHOR]

For calibrating the measurement error of the speed sensor and estimating the speed accurately while system is nonlinear and noise is non-Gaussian, dual radar and wheel axle sensor are configured and federal particle filter is used to acquire fusion. The whole operation process of CRH3 train from start to stop between two stations is taken as an example, the speed measurement errors of different speed estimation methods under the conditions of train idling, slipping, vibration and the speed sensor with dynamic noise are analyzed and verified. The simulation results show that: The root-mean-square error can be reduced by 31.52% and 47.35% respectively in the idling and sliding stages after the calibration of dual-radar and wheel-axle combination compared with no calibration; Compared with the two types of dual-radar calibration methods, the maximum relative error of the dual-radar separation vibration speed calibration method can be 39.66% lower than that of the dual-radar angle deviation estimation calibration method when the train vibration speed ratio is 0 to 1 and the radar installation angle error is -1° to 1°. Compared with the filtering results of dual radar and axle sensors before fusion, the speed measurement results after using joint particle filtering fusion are 34.71% and 14.03% lower in MAE, and 26.51% and 10.98% lower in RMSE respectively. Compared with federated extended Kalman filter fusion, the root mean square error of velocity measurement using federated particle filter fusion is 26.97% lower and the maximum absolute error can be reduced by 16.10%. [ABSTRACT FROM AUTHOR]

In this study, we present a novel approach for the real-time detection of high-speed moving objects with rapidly changing velocities using a high-resolution millimeter-wave (MMW) radar operating at 94 GHz in the W-band. Our detection methodology leverages continuous wave transmission and heterodyning of the reflected signal from the moving target, enabling the extraction of motion-related attributes such as velocity, position, and physical characteristics of the object. The use of a 94 GHz carrier frequency allows for high-resolution velocity detection with a velocity resolution of 6.38 m/s, achieved using a short integration time of 0.25 ms. This high-frequency operation also results in minimal atmospheric absorption, further enhancing the efficiency and effectiveness of the detection process. The proposed system utilizes cost-effective and less complex equipment, including compact antennas, made possible by the low sampling rate required for processing the intermediate frequency signal. The experimental results demonstrate the successful detection and characterization of high-speed moving objects with high acceleration rates, highlighting the potential of this approach for various scientific, industrial, and safety applications, particularly those involving targets with rapidly changing velocities. The detailed analysis of the micro-Doppler signatures associated with these objects provides valuable insights into their unique motion dynamics, paving the way for improved tracking and classification algorithms in fields such as aerospace research, meteorology, and collision avoidance systems. [ABSTRACT FROM AUTHOR]

The application and uses of drones in all areas are continuously rising, especially in civilian use cases. This increasing threat requires reliable drone surveillance in urban environments. Radar is the obvious candidate with its ability to detect small objects at range, in all weather conditions. The use of an L‐band networked radar for urban sensing of S‐UAS targets is explored. Small echoes from S‐UAS places a premium on synchronisation, which is the fundamental key for high performance networked radar. The effect of timing errors on the operation of the network radar is investigated theoretically and experimentally, and the processing tools for synchronising data based on the direct signal returns of the transmitter are developed. Also, drone detection using bistatic L‐band staring radar is achieved both in simulation and then in real field trials where the SNR and detection performance are computed and analysed. The updated direct signal synchronisation method for bistatic staring radar is shown to provide comparable SNR and positional accuracy for S‐UAS targets as the monostatic staring radar. [ABSTRACT FROM AUTHOR]

The large quantity of resident space objects orbiting Earth poses a threat to safety and efficient operations in space. Radar sensors are well suited to detecting objects in space including decommissioned satellites and debris, whereas the more commonly used optical sensors are limited by daylight and weather conditions. Observations of three non-operational satellites using a VHF radar system are presented in this paper in the form of micro Doppler signatures associated with rotational motion. Micro Doppler signatures are particularly useful for characterising resident space objects at VHF given the limited bandwidth resulting in poor range resolution. Electromagnetic simulations of the micro Doppler signatures of the defunct satellites are also presented using simple computer-aided design (CAD) models to assist with interpretation of the radar observations. The simulated micro Doppler results are verified using the VHF radar data and provide insight into the attitude and spin axis of the three resident space objects. As future work, this approach will be extended to a larger number of resident space objects which requires a automated processing. [ABSTRACT FROM AUTHOR]

Hailstorms, while fascinating from a meteorological perspective, pose significant risks to communities, agriculture, and infrastructure. In regions such as the Central Andes of Peru, the characteristics and frequency of these extreme weather events remain largely uncharted. This study fills this gap by investigating the historical frequency and vertical structure of hailstorms in this region. We analyzed historical hailstorm records dating back to 1958 alongside 4 years of observations (2017–2021) from the Parsivel2 disdrometer and a cloud-profiling radar MIRA35c. Our findings indicate a trend of decreasing hail frequency (- 0.5 events per decade). However, the p value of 0.07 suggests the need for further investigation, particularly in relation to environmental changes and reporting methods. The results show that hailstorms predominantly occur during the austral summer months, with peak frequency in December, and are most common during the afternoon and early evening hours. The analysis of radar variables such as reflectivity, radial velocity, spectral width, and linear depolarization ratio (LDR) reveals distinct vertical profiles for hail events. Two case studies highlight the diversity in the radar measurements of hailstorms, underscoring the complexity of accurate hail detection. This study suggests the need for refining the Parsivel2 algorithm and further understanding its classification of hydrometeors. Additionally, the limitations of conventional radar variables for hail detection are discussed, recommending the use of LDR and Doppler spectrum analysis for future research. Our findings lay the groundwork for the development of more efficient hail detection algorithms and improved understanding of hailstorms in the Central Andes of Peru. [ABSTRACT FROM AUTHOR]

The potential of microwave Doppler radar in non-contact vital sign detection is significant; however, prevailing radar-based heart rate (HR) and heart rate variability (HRV) monitoring technologies often necessitate data lengths surpassing 10 s, leading to increased detection latency and inaccurate HRV estimates. To address this problem, this paper introduces a novel network integrating a frequency representation module and a residual in residual module for the precise estimation and tracking of HR from concise time series, followed by HRV monitoring. The network adeptly transforms radar signals from the time domain to the frequency domain, yielding high-resolution spectrum representation within specified frequency intervals. This significantly reduces latency and improves HRV estimation accuracy by using data that are only 4 s in length. This study uses simulation data, Frequency-Modulated Continuous-Wave radar-measured data, and Continuous-Wave radar data to validate the model. Experimental results show that despite the shortened data length, the average heart rate measurement accuracy of the algorithm remains above 95% with no loss of estimation accuracy. This study contributes an efficient heart rate variability estimation algorithm to the domain of non-contact vital sign detection, offering significant practical application value. [ABSTRACT FROM AUTHOR]

This paper analyzes the beneficial effects on phase detection arising from the motion of an ac-coupled Doppler radar. Indeed, although the presence of an ac coupling stage suppresses the dc offset after the receiver RF output, due to the coupling capacitor, a high-pass behavior is introduced; the presence of a high-pass behavior leads to signal distortion, particularly for low Doppler frequencies, which are typical in many biomedical or industrial applications. Since the target displacement is usually extracted from the phase history, this effect might, in turn, worsen the overall accuracy of the system. Moreover, if the target alternates stationary and moving time intervals, the phase detection step becomes challenging. Indeed, during the stationary time, the output of the RF front-end shows only noise fluctuations that, in turn, result in uncorrelated phases which might be confused with the real target displacement. This negative effect might be avoided by keeping the radar continuously moving, thus exploiting what is usually considered a state that is negative and worthy of attention. In this contribution, this effect is addressed from a different perspective, and ad hoc experimental case studies are shown to demonstrate the effectiveness of the proposed system. This task has been accomplished through theoretical analysis and related experimental activity. [ABSTRACT FROM AUTHOR]

The authors present multiple machine learning‐based methods for distinguishing maritime targets from sea clutter. The main goal for this classification framework is to aid future millimetre wave radar system design for marine autonomy. Availability of empirical data at this frequency range in the literature is scarce. The classification and anomaly detection techniques reported here use experimental data collected from three different field trials from three different millimetre wave radars. Two W‐band radars operating at 77 and 94 GHz and a G‐band radar operating at 207 GHz were used for the field trial data collection. The dataset encompasses eight classes including sea clutter returns. The other targets are boat, stand up paddleboard/kayak, swimmer, buoy, pallet, stationary solid object (i.e. rock) and sea lion. The Doppler signatures of the targets have been investigated to generate feature values. Five feature values have been extracted from Doppler spectra and four feature values from Doppler spectrograms. The features were trained on a supervised learning model for classification as well as an unsupervised model for anomaly detection. The supervised learning was performed for both multi‐class and 2‐class (sea clutter and target) classification. The classification based on spectrum features provided an 84.3% and 80.1% validation and test accuracy respectively for the multi‐class classification. For the spectrogram feature‐based learning, the validation and test accuracy for multi‐class increased to 93.3% and 88.7% respectively. For the 2‐class classification, the spectrum feature‐based training accuracies are 88.1% and 86.8%, whereas with the spectrogram feature‐based model, the values are 95% and 94.1% for validation and test accuracies respectively. A one class support vector machine was also applied to an unlabelled dataset for anomaly detection training, with 10% outlier data. The cross‐validation accuracy has shown very good agreement with the expected anomaly detection rate. [ABSTRACT FROM AUTHOR]

Abstract Urban surveillance of slow‐moving small targets such as drones and birds in low to medium airspace using radar presents significant challenges. Detecting, locating and identifying such low observable targets in strong clutter requires both innovation in radar hardware design and optimisation of processing algorithms. To this end, the University of Birmingham (UoB) has set‐up a testbed of two L‐band staring radars to support performance benchmarking using datasets of target and clutter from realistic urban environment. This testbed is also providing the vehicle to understand how novel radar architectures can enhance radar capabilities. Some of the challenges in installing the radar at the UoB campus are highligted. Detailed benchmarking results are provided from urban monostatic and bistatic field trials that form the basis for performance comparison against future hardware modification. The solution to the challenge of interfacing the radar to the external oscillators is described and stand‐alone bench tests with the candidate oscillators are reported. The testbed provides a valuable capability to undertake detailed analysis of performance of Quantum photonic‐enabled radar and allows for its comparison with conventional oscillator technology for surveillance of low observable targets in the presence of urban clutter.

Hand gesture recognition, which is one of the fields of human–computer interaction (HCI) research, extracts the user's pattern using sensors. Radio detection and ranging (RADAR) sensors are robust under severe environments and convenient to use for hand gestures. The existing studies mostly adopted continuous-wave (CW) radar, which only shows a good performance at a fixed distance, which is due to its limitation of not seeing the distance. This paper proposes a hand gesture recognition system that utilizes frequency-shift keying (FSK) radar, allowing for a recognition method that can work at the various distances between a radar sensor and a user. The proposed system adopts a convolutional neural network (CNN) model for the recognition. From the experimental results, the proposed recognition system covers the range from 30 cm to 180 cm and shows an accuracy of 93.67% over the entire range. [ABSTRACT FROM AUTHOR]

Urban surveillance of slow‐moving small targets such as drones and birds in low to medium airspace using radar presents significant challenges. Detecting, locating and identifying such low observable targets in strong clutter requires both innovation in radar hardware design and optimisation of processing algorithms. To this end, the University of Birmingham (UoB) has set‐up a testbed of two L‐band staring radars to support performance benchmarking using datasets of target and clutter from realistic urban environment. This testbed is also providing the vehicle to understand how novel radar architectures can enhance radar capabilities. Some of the challenges in installing the radar at the UoB campus are highligted. Detailed benchmarking results are provided from urban monostatic and bistatic field trials that form the basis for performance comparison against future hardware modification. The solution to the challenge of interfacing the radar to the external oscillators is described and stand‐alone bench tests with the candidate oscillators are reported. The testbed provides a valuable capability to undertake detailed analysis of performance of Quantum photonic‐enabled radar and allows for its comparison with conventional oscillator technology for surveillance of low observable targets in the presence of urban clutter. [ABSTRACT FROM AUTHOR]

The millimetre wave radar signatures of sea lions collected from three animals in the outdoor seal pool available at the Sea Mammal Research Unit in St Andrews in the Autumn of 2021 is reported. The objective is to study the radar amplitude and Doppler signatures of the animals when their full body or part thereof is above water, which is important for the application of autonomous marine navigation. The data was collected using 24 GHz (K‐band) and 77 GHz (W‐band) Frequency Modulated Continuous Wave radars with linear polarisation. It has been demonstrated that the sea lions were very clearly detected by the radars with Signal to Noise Ratio greater than 30 dB at a range of 40 m. The calculated modal radar cross section (RCS) of the sea lions in HH polarisation at 24 and 77 GHz vary from −48 to −26 dBsm and −48 to −28 dBsm respectively, corresponding to the different body parts and the amount of exposure to the radar beam. In VV polarisation, the modal RCS value range is from −49 to −26 dBsm and −49 to −22 dBsm respectively. The corresponding maximum RCS and the Cumulative Distribution Function results are also reported. [ABSTRACT FROM AUTHOR]

This paper comprehensively analyzed two consecutive tornado events associated with heavy precipitation during the Mei-yu season (a period of continuous rainy weather that occurs in the middle and lower reaches of the Yangtze River in China from mid-June to mid-July each year) and detailed the formation and development process of the tornadoes using Doppler weather radar, wind profiler radar, ERA5 reanalysis data, ground automatic station data and other multi-source data. The results showed that: (1) Small-scale vortices were triggered and developed during the eastward movement of the low vortex, forming two tornadoes successively on the eastern section of the Mei-yu front. (2) The presence of a gap on the front side of the reflectivity factor profile indicated that strong incoming airflow entered the updraft. Mesocyclones were detected with decreasing heights and increasing shear strengths. The bottom height of the tornado vortex signature (TVS) dropped to 0.7 km, and the shear value increased to 55.4 × 10−3 s−1. Tornado debris signatures (TDSs) could be seen with a low cross-correlation coefficient (CC) value area of 0.85–0.9 in the mesocyclone. The difference between the lowest-level difference velocity (LLDV) and the maximum difference velocity (MXDV) reached the largest value when a tornado occurred. (3) The continuously enhanced low-level jet propagated downward to form a super-low-level jet, and the strong wind direction and wind speed convergence in the boundary layer created a warm, moist and unstable atmosphere in Suzhou. With the entrainment of dry air, the northwest dry jet and the southeast moist jet stimulated the formation of a miniature supercell. (4) The low-level vertical wind shear of 0–1 km increased significantly upon tornado occurrence, which was more conducive to the formation and intensification of horizontal vorticity tubes. Encountering updrafts and downdrafts, the vorticity tubes might have been stretched and intensified. The first lightning jumps appeared 15 min and 66 min earlier than the Kunshan Bacheng tornado and the Taicang Liuhe tornado. The Liuhe tornado occurred during the stage when the lightning frequency reached its peak and then fell back. [ABSTRACT FROM AUTHOR]

We present a comprehensive quality-controlled 15-month dataset of remote sensing observations of low-level mixed-phase clouds (LLMPCs) taken at the high Arctic site of Ny-Ålesund, Svalbard, Norway. LLMPCs occur frequently in the Arctic region and extensively affect the energy budget. However, our understanding of the ice microphysical processes taking place in these clouds is incomplete. The dual-wavelength and polarimetric Doppler cloud radar observations, which are the cornerstones of the dataset, provide valuable fingerprints of ice microphysical processes, and the high number of cases included allows for the compiling of robust statistics for process studies. The radar data are complemented with thermodynamic retrievals from a microwave radiometer, liquid base height from a ceilometer, and wind fields from large-eddy simulations. All data are quality controlled, especially the cloud radar data, which are accurately calibrated, matched, and corrected for gas and liquid-hydrometeor attenuation, ground clutter, and range folding. We finally present an analysis of the temperature dependence of Doppler, dual-wavelength, and polarimetric radar variables, to illustrate how the dataset can be used for cloud microphysical studies. The dataset has been published in and is freely available at: 10.5281/zenodo.7803064. [ABSTRACT FROM AUTHOR]

The interaction of airflow with complex terrain has the potential to significantly amplify extreme precipitation events and modify the structure and intensity of precipitating cloud systems. However, understanding and forecasting such events is challenging, in part due to the scarcity of direct in situ measurements. Doppler radar can provide the capability to monitor extreme rainfall events over land, but our understanding of airflow modulated by orographic interactions remains limited. The SAMURAI software is a three-dimensional variational data assimilation (3DVAR) technique that uses the finite element approach to retrieve kinematic and thermodynamic fields. The analysis has high fidelity to observations when retrieving flows over a flat surface, but the capability of imposing topography as a boundary constraint is not previously implemented. Here, we implement the immersed boundary method (IBM) as pseudo-observations at their native coordinates in SAMURAI to represent the topographic forcing and surface impermeability. In this technique, neither data interpolation onto a Cartesian grid nor explicit physical constraint integration during the cost function minimization is needed. Furthermore, the physical constraints are treated as pseudo-observations, offering the flexibility to adjust the strength of the boundary condition. A series of observing simulation sensitivity experiments (OSSEs) using a full-physics model and radar emulator simulating rainfall from Typhoon Chanthu (2021) over Taiwan are conducted to evaluate the retrieval accuracy and parameter settings. The OSSE results show that the strength of the IBM constraints can impact the overall wind retrievals. Analysis from real radar observations further demonstrates that the improved retrieval technique can advance scientific analyses for the underlying dynamics of orographic precipitation using radar observations. [ABSTRACT FROM AUTHOR]

The usual procedure for extracting ocean surface information from data acquired from a high‐frequency surface wave radar transmitting from a floating platform is to first compensate for the motion of the antenna in the acquired motion‐contaminated Doppler spectrum and then extract the ocean wave parameters from the motion‐compensated result. The authors propose a new real‐time method to estimate the significant wave height directly from the antenna's received electric field in the time‐domain without requiring prior knowledge of the motion parameters or performing motion compensation. Based on the relation between the ocean surface displacement and the received electric field, this method calculates the significant wave height from the windowed variance of the upper envelope of the received electric field. A preliminary calibration is required, which can be carried out either by the deployment of a wave buoy or by analysing the data over a time period during which the sea state varies. The results from this simple proposed technique show that it may be used to estimate the significant wave height with a root‐mean‐square error (RMSE) between 10 and 14 cm over a wide range of significant wave height values. [ABSTRACT FROM AUTHOR]

Abstract Hand gesture recognition based on micro‐Doppler (MD) radar has garnered considerable attention from researchers as a potential method for human–computer interaction (HCI). However, two significant challenges, that is, obvious differences in MD map size and lots of redundant information contained in the MD maps, are encountered. Here, a multi‐scale graph‐based hand gesture recognition framework is proposed. First, a MD graph representation method is developed, which is adapted to arbitrary‐sized frames and enables to map the key features in a sparse manner. Then, the multi‐scale information from MD graph is fully extracted for hand gesture recognition. Experimental results show that the proposed framework achieves a state‐of‐the‐art accuracy of 96.73% on the four‐class radar hand gesture dataset, while reducing up to 99% of the redundant information in the MD maps. This framework requires only a little amount of memory storage with good hand gesture recognition capability, demonstrating its high potential in the HCI field.

Hand gesture recognition based on micro‐Doppler (MD) radar has garnered considerable attention from researchers as a potential method for human–computer interaction (HCI). However, two significant challenges, that is, obvious differences in MD map size and lots of redundant information contained in the MD maps, are encountered. Here, a multi‐scale graph‐based hand gesture recognition framework is proposed. First, a MD graph representation method is developed, which is adapted to arbitrary‐sized frames and enables to map the key features in a sparse manner. Then, the multi‐scale information from MD graph is fully extracted for hand gesture recognition. Experimental results show that the proposed framework achieves a state‐of‐the‐art accuracy of 96.73% on the four‐class radar hand gesture dataset, while reducing up to 99% of the redundant information in the MD maps. This framework requires only a little amount of memory storage with good hand gesture recognition capability, demonstrating its high potential in the HCI field. [ABSTRACT FROM AUTHOR]

Background: Beyond causing significant morbidity and cost, musculoskeletal injuries (MSKI) are among the most common reasons for primary care visits. A validated injury risk assessment tool for MSKI is conspicuously absent from current care. While motion capture (MC) systems are the current gold standard for assessing human motion, their disadvantages include large size, non-portability, high cost, and limited spatial resolution. As an alternative we introduce the Micro Doppler Radar (MDR); in contrast with MC, it is small, portable, inexpensive, and has superior spatial resolution capabilities. While Phase 1 testing has confirmed that MDR can identify individuals at high risk for MSKI, Phase 2 testing is still needed. Our aims are to 1) Use MDR technology and MC to identify individuals at high-risk for MSKI 2) Evaluate whether MDR has diagnostic accuracy superior to MC 3) Develop MDR algorithms that enhance accuracy and enable automation. Methods and findings: A case control study will compare the movement patterns of 125 ACL reconstruction patients to 125 healthy controls. This study was reviewed and approved by the Pennsylvania State University Human Research Protection Program (HRPP) on May 18, 2022, and the IRB approval number is STUDY00020118. The ACL group is used as a model for a "high risk" population as up to 24% will have a repeat surgery within 2 years. An 8-camera Motion Analysis MC system with Cortex 8 software to collect MC data. Components for the radar technology will be purchased, assembled, and packaged. A micro-doppler signature projection algorithm will determine correct classification of ACL versus healthy control. Our previously tested algorithm for processing the MDR data will be used to identify the two groups. Discrimination, sensitivity and specificity will be calculated to compare the accuracy of MDR to MC in identifying the two groups. Conclusions: We describe the rationale and methodology of a case-control study using novel MDR technology to detect individuals at high-risk for MSKI. We expect this novel approach to exhibit superior accuracy than the current gold standard. Future translational studies will determine utility in the context of clinical primary care. [ABSTRACT FROM AUTHOR]

With the development of autonomous driving and the emergence of various intelligent traffic scenarios, object detection technology based on deep learning is more and more widely applied to real traffic scenarios. Commonly used detection devices include LiDAR and cameras. Since the implementation of traffic scene target detection technology requires mass production, the advantages of millimeter-wave radar have emerged, such as low cost and no interference from the external environment. The performance of LiDAR and cameras is greatly reduced due to their sensitivity to light, which affects target detection at night and in bad weather. However, millimeter-wave radar can overcome the influence of these harsh environments and has a great auxiliary effect on safe driving on the road. In this work, we propose a deep-learning-based object detection method considering the radar range–Doppler spectrum in traffic scenarios. The algorithm uses YOLOv8 as the basic architecture, makes full use of the time series characteristics of range–Doppler spectrum data in traffic scenarios, introduces the ConvLSTM network, and exerts the ability to process time series data. In order to improve the model's ability to detect small objects, an efficient and lightweight Efficient Channel Attention (ECA) module is introduced. Through extensive experiments, our model shows better performance on two publicly available radar datasets, CARRADA and RADDet, compared to other state-of-the-art methods. Compared with other mainstream methods that can only achieve 30–60% mAP performance when the IOU is 0.3, our model can achieve 74.51% and 75.62% on the RADDet and CARRADA datasets, respectively, and has better robustness and generalization ability. [ABSTRACT FROM AUTHOR]

The south polar layered deposits (SPLD) of Mars record its Amazonian climate history. Detailed stratigraphic analyses of the SPLD will improve our understanding of the Martian depositional system. Radar reflection patterns correlate with the varying composition of the internal layers, thus providing a key to unlocking the depositional history of the SPLD and Martian paleoclimate. Using data from SHAllow RADar (SHARAD) onboard the Mars Reconnaissance Orbiter, we studied a new type of weak reflection pattern (shadow zone) across the SPLD. These shadow zones lack identifiable bases and are generally deeper than previously reported low‐reflection zones interpreted as sealed CO2 ice. The discrepancy between the waveforms of shadow zones in focused and unfocused radargrams was used to identify whether the shadow zones were associated with fog‐related data‐processing artifacts. Numerical simulations utilizing various dielectric models demonstrate that lossy materials are most likely the cause of the shadow zone. These materials are expected to exhibit a gradual increase in concentration with depth, ranging from pure water ice to lossy materials at a scale of >50 m. The presence of such lossy materials is consistent with the compositional heterogeneity of SPLD, but their origins remain obscure. Plain Language Summary: Orbital radar data reveal diverse reflection patterns within the south polar layered deposits (SPLD) of Mars, providing valuable insights into their internal structure and composition. The reflection powers are low or even non‐existent when there is weak compositional variation or the material is lossy. This study reports and investigates a low‐reflection zone, referred to as the shadow zone, in the SHAllow RADar (SHARAD) data across the SPLD. The shadow zones exist in the background of a diffuse echo signature known as radar fog. Compared to previously reported low‐reflection zones, shadow zones typically lack a detectable base and exhibit significantly greater depth. We analyzed the waveform of the SHARAD radargrams and the results indicate the presence of two distinct types of shadow zones: one attributed to fog‐related artifacts, and the other classified as real shadow zones. Numerical simulations indicate the real shadow zones are likely linked to lossy materials such as salts, clays, and/or mafic minerals. These materials are expected to exhibit a distribution of gradual increasing concentration with depth in the SPLD. This research provides new insights into the internal composition of the SPLD and its potential relationship with the climate of Mars. Key Points: We explored shadow zones in SHAllow RADar data poleward at 80°S using radargrams before and after synthetic aperture processingThe shadow zones exhibit distinct morphologies from the previously identified reflectivity‐free zones, which were inferred to be dry iceForward modeling reveals that shadow zones likely associate with lossy materials conforming to a gradual increasing concentration with depth [ABSTRACT FROM AUTHOR]

Volcanic emissions (ash, gas, aerosols) dispersed in the atmosphere during explosive eruptions generate hazards affecting aviation, human health, air quality, and the environment. We document for the first time the contamination of airspace by very fine volcanic ash due to sequences of transient ash plumes from Mount Etna. The atmospheric dispersal of sub-10 μm (PM10) ash is modelled using the WRF-Chem model, coupled online with meteorology and aerosols and offline with mass eruption rates (MERs) derived from near-vent Doppler radar measurements and inferred plume altitudes. We analyze two sequences of paroxysms with widely varied volcanological conditions and contrasted meteorological synoptic patterns in October–December 2013 and on 3–5 December 2015. We analyze the PM10 ash dispersal simulation maps in terms of time-averaged columnar ash density, concentration at specified flight levels averaged over the entire sequence interval, and daily average concentration during selected paroxysm days at these flight levels. The very fine ash from such eruption sequences is shown to easily contaminate the airspace around the volcano within a radius of about 1000 km in a matter of a few days. Synoptic patterns with relatively weak tropospheric currents lead to the accumulation of PM10 ash at a regional scale all around Etna. In this context, closely interspersed paroxysms tend to accumulate very fine ash more diffusively at a lower troposphere and in stretched ash clouds higher up in the troposphere. Low-pressure, high-winds weather systems tend to stretch ash clouds into ~100 km wide clouds, forming large-scale vortices 800–1600 km in diameter. Daily average PM10 ash concentrations commonly exceed the aviation hazard threshold, up to 1000 km downwind from the volcano and up to the upper troposphere for intense paroxysms. Vertical distributions show ash cloud thicknesses in the range 0.7–3 km, and PM10 sometimes stagnates at ground level, which represent a potential health hazard. [ABSTRACT FROM AUTHOR]

The authors show improved radar range and velocity resolution is achieved using fast orthogonal search in place of the standard fast Fourier transform. The method reliably detects targets that are close either in range or in velocity, which is relevant today given recent advances in target swarms. The method presented also allows radars to detect slower targets closer to the zero‐Doppler region, like uncrewed aerial systems (drones) moving slowly to escape detection. The authors also show a novel method for estimating the velocity spectrum of a pulse‐Doppler radar using the in‐phase and quadrature samples directly by presenting unique terms to the fast orthogonal search. This eliminates the need to convert into complex samples for processing. The authors compared the performance of the new fast orthogonal search method against the fast Fourier transform using both simulated pulse‐Doppler radar data and real frequency‐modulated continuous wave radar data taken in an anechoic chamber. The simulated pulse‐Doppler scenarios included targets closely spaced in velocity and targets near the zero‐Doppler null, all at a variety of signal‐to‐interference ratios. In all cases, the fast orthogonal search method was shown capable of detecting both the closely‐spaced and near‐zero‐Doppler targets a greater percentage of time compared to the fast Fourier transform. Furthermore, when the methods were compared using real frequency‐modulated continuous wave radar data, the stationary targets were resolved at 0.4 m using the fast orthogonal search as compared to 0.6 m for the fast Fourier transform. Thus, this new method is capable of greater radar range and velocity resolution than the fast Fourier transform. [ABSTRACT FROM AUTHOR]

A radar altimeter (RA) is useful to improve autonomous functions such as landing guidance or navigation control of an aircraft. To ensure more precise and safer flights by aircraft, an interferometric RA (IRA) capable of measuring the angle of a target is required. However, the phase-comparison monopulse (PCM) technique used in IRAs has a problem in that an angular ambiguity arises with respect to a target with multiple reflection points, such as terrain. In this paper, we propose an altimetry method for IRAs that reduces the angular ambiguity by evaluating the quality of the phase. The altimetry method as introduced here is sequentially described based on synthetic aperture radar, a delay/Doppler radar altimeter, and PCM techniques. Finally, a phase quality evaluation method is proposed for use in the azimuth estimation process. Aircraft captive flight test results are presented and analyzed, and the validity of the proposed method is examined. [ABSTRACT FROM AUTHOR]

Capabilities to directly assimilate radar data are implemented within the local ensemble transform Kalman filter (LETKF) and the gain‐form LETKF (LGETKF) algorithms of the Joint Effort for Data assimilation Integration (JEDI) system. The capabilities are evaluated for the analysis and forecast of a severe convection case of 20 May 2019 in the Southern Great Plains using the limited area model version of the FV3 dynamical core (FV3‐LAM) from a recent release for Short‐Range Weather Application (SRW App). The LETKF and LGETKF implementations are shown to produce analyses and short‐range forecasts comparable to those using the ensemble square‐root Kalman Filter (EnSRF) within the Gridpoint Statistical Interpolation (GSI) framework used by current NCEP operational models. In addition, LGETKF retaining only 60% variances for model‐space vertical localization performs similarly to LGETKF retaining 99% of variance and LETKF using observation error‐based vertical localization. JEDI LETKF shows better parallel scalability than LGETKF and GSI EnSRF. Plain Language Summary: The JEDI data assimilation (DA) framework under development will be used by all operational forecasting systems of the U.S. National Weather Service, including the Rapid Refresh Forecast System based on FV3, a dynamic core chosen for next‐generation NWS forecasting systems. In this study, we implemented radar DA capabilities into the JEDI with two local ensemble transform Kalman filter (LETKF) algorithms coupled with limited‐area FV3. Analyses and forecasts with radar DA better match radar reflectivity and radial velocity observations than forecasts without radar DA during the assimilation period. Three‐hour forecasts of precipitation, reflectivity, and updraft rotation show skills comparable to a DA experiment using the ensemble square‐root Kalman filter within the more mature GSI DA system. In addition, the new gain‐form LETKF, which has not been applied to radar DA before, performs quite well, although it is computationally more expensive than the regular LETKF. This work paves the way for using JEDI to assimilate radar data in operational forecasting systems. Key Points: Radar data assimilation capabilities are implemented and tested in Joint Effort for Data assimilation Integration (JEDI), a future data assimilation infrastructure for Unified Forecast System applicationsLocal ensemble transform Kalman filter (LETKF) algorithm with gain‐form vertical localization is tested for radar data assimilation and is found to perform similarly as LETKFThe enhanced gain‐form LETKF and LETKF in JEDI perform similarly as ensemble square‐root Kalman Filter within Gridpoint Statistical Interpolation for radar data assimilation [ABSTRACT FROM AUTHOR]

The cloud-profiling radar (CPR) on the Earth Clouds, Aerosol, and Radiation Explorer (EarthCARE) satellite (EC-CPR) is the first satellite-borne Doppler radar. In a previous study, we examined the effects of horizontal (along-track) integration and simple unfolding methods on the reduction of Doppler errors in the EC-CPR observations, and those effects were evaluated using two limited scenes in limited-latitude and low-pulse-repetition-frequency (PRF) settings. In this study, the amount of data used was significantly increased, and the area of the data used was extended globally. Not only low-PRF but also high-PRF settings were examined. We calculated the EC-CPR-observed Doppler velocity from pulse-pair covariances using the radar reflectivity factor and Doppler velocity obtained from a satellite data simulator and a global storm-resolving simulation. The global data were divided into five latitudinal zones, and each standard deviation of Doppler errors for 5 dB Ze after 10 km integration was calculated. In the case of the low-PRF setting, the error without unfolding correction for the tropics reached a maximum of 2.2 m s -1 and then decreased toward the poles (0.43 m s -1). The error with unfolding correction for the tropics became much smaller at 0.63 m s -1. In the case of the high-PRF setting, the error without unfolding correction for the tropics reached a maximum of 0.78 m s -1 and then decreased toward the poles (0.19 m s -1). The error with unfolding correction for the tropics was 0.29 m s -1 , less than half the value without the correction. The results of the analyses of the simulated data indicated that the zonal mean frequency of precipitation echoes was highest in the tropics and decreased toward the poles. Considering a limitation of the unfolding correction for discrimination between large upward velocity and large precipitation falling velocity, the latitudinal variation in the standard deviation of Doppler error can be explained by the precipitation echo distribution. [ABSTRACT FROM AUTHOR]

Hailstorms, while fascinating from a meteorological perspective, pose significant risks to communities, agriculture, and infrastructure. In regions such as the Central Andes of Peru, the characteristics and frequency of these extreme weather events remain largely uncharted. This study fills this gap by investigating the historical frequency and vertical structure of hailstorms in this region. We analyzed historical hailstorm records dating back to 1958 alongside four years of observations (2017–2021) from Parsivel2 disdrometer and a cloud profiler radar MIRA35c. Our findings indicate a trend of decreasing hail frequency (-0.5 events/decade). However, the p-value of 0.07 suggests the need for further investigation, particularly in relation to environmental changes and reporting methods. The results show that hailstorms predominantly occur during the austral summer months, with peak frequency in December, and are most common during the afternoon and early evening hours. The analysis of radar variables such as reflectivity, radial velocity, spectral width, and linear depolarization ratio (LDR) reveals distinct vertical profiles for hail events. Two case studies highlight the diversity in the radar measurements of hailstorms, underscoring the complexity of accurate hail detection. This study suggests the necessity for refining the Parsivel2 algorithm and further understanding its classification of hydrometeors. Additionally, the limitations of conventional radar variables for hail detection are discussed, recommending the use of LDR and Doppler spectrum analysis for future research. Our findings lay the groundwork for the development of more efficient hail detection algorithms and improved understanding of hailstorms in the Central Andes of Peru. [ABSTRACT FROM AUTHOR]

Detailed within is an attempt to implement a real-time radar signal classification system to monitor and count bee activity at the hive entry. There is interest in keeping records of the productivity of honeybees. Activity at the entrance can be a good measure of overall health and capacity, and a radar-based approach could be cheap, low power, and versatile, beyond other techniques. Fully automated systems would enable simultaneous, large-scale capturing of bee activity patterns from multiple hives, providing vital data for ecological research and business practice improvement. Data from a Doppler radar were gathered from managed beehives on a farm. Recordings were split into 0.4 s windows, and Log Area Ratios (LARs) were computed from the data. Support vector machine models were trained to recognize flight behavior from the LARs, using visual confirmation recorded by a camera. Spectrogram deep learning was also investigated using the same data. Once complete, this process would allow for removing the camera and accurately counting the events by radar-based machine learning alone. Challenging signals from more complex bee flights hindered progress. System accuracy of 70% was achieved, but clutter impacted the overall results requiring intelligent filtering to remove environmental effects from the data. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The ever‐expanding horizon of radar applications demands solutions with high‐end radar functionalities and technologies and is often limited by the available radar equipment, cost and time. A practical method to tackle the situation is to rely on the modelling and simulation of radar systems based on the user requirements. The comprehensive system‐level modelling of a pulsed Doppler radar in MATLAB/Simulink consisting of all the fundamental blocks in the transmit chain, the environment, the receive chain, and the data processing chain is presented in this article. The first half of the article discusses the high‐fidelity simulation of each building block in the radar model. In the second half of the article, the range‐Doppler plot generated from the high‐fidelity radar model is compared and validated using the range‐Doppler plot from a real radar trial. The radar phase noise plays a crucial role in the detection of slowly moving, low radar cross‐section targets in the presence of strong clutter. The article also briefly discusses the effects of radar oscillator phase noise in the range‐Doppler plot. The validated, fully flexible radar model has the advantage of supporting the addition of further building blocks and optimising the parameters based on user requirements. [ABSTRACT FROM AUTHOR]

Earth's ionosphere has long been targeted as a medium for remote sensing of explosive terrestrial events such as earthquakes, volcanic eruptions, and nuclear/conventional weapon detonations. Until now, the only confirmed ionospheric detections have been of very large events that were easily detectable through other traditional global sensor systems (e.g., seismic). We present the first clear, confirmed detections of relatively low yield 1‐ton TNT‐equivalent chemical explosions using pulsed Doppler radar observations of isodensity layers in the ionospheric E region. The shape and spectra of the detected waveforms closely match predictions from the acoustic ray tracing and weakly nonlinear waveform propagation models. The explosions were roughly three orders of magnitude lower yield than any previous confirmed ionospheric detection and represent the first conclusive evidence that explosions of this size can have clear impacts on the ionosphere. This technique could improve the remote detection of both anthropogenic and natural explosive events. Plain Language Summary: Explosions, even of modest yield, generate copious amounts of acoustic waves across a wide range of frequencies. While the high frequencies are quickly absorbed by the atmosphere, the lowest frequencies, in the sub‐audible infrasound part of the spectrum, are capable of traveling to great distances. Typically this infrasound is detected on the ground using highly sensitive listening equipment such as microbarometers. We present a different method, however, where we utilize the fact that the upper atmosphere is ionized (the ionosphere) and is therefore a good target for a radar. As the sound wave passes through the ionosphere it sloshes the bulk ions such that it shows up an oscillating radar target. We use this new technique to clearly detect explosions with yields of 1‐ton TNT equivalent. These detections are 500 times smaller than previous confirmed detections using the ionosphere as a sensor for explosions. Key Points: Infrasound from 1‐ton TNT‐equivalent chemical explosions are clearly detectable using pulsed Doppler radar of the ionosphereObservations match the modeled temporal and spectral predictions of infrasound propagationStrong azimuthal differences in waveform shape and strength could be caused by uneven terrain or complex wind fields [ABSTRACT FROM AUTHOR]

In this study, a 1 W W-band solid-state power amplifier (SSPA) was developed for application in a W-band compact pulse Doppler radar. The SSPA up-converted the X-band signal to a W-band signal with a multiplier (×8) and then amplified the high output using an amplifier. To apply the W-band compact radar, which transmits and receives signals using a single antenna, the SSPA noise was reduced by controlling the drain voltage of the SSPA according to the pulse repetition frequencies. The average power obtained was in the range of 31.7∼31.8 dBm, and stable operation was possible for more than 50 s. [ABSTRACT FROM AUTHOR]

The assimilation of Doppler radar radial wind observations is important for improving tropical cyclone (TC) prediction. However, the specific impacts and contributions of these observations over different areas are still unclear. In this study, the impact of assimilating radar radial winds within the inner‐ and outer‐cores of TCs is evaluated. For TC Mujigae (2015), analyses suggest that although the outer‐core observations better improve the upper‐level outflow of the TC, the inner‐core observations contribute more to enhancing tangential wind and updraft near the eyewall as well as correcting the TC size, warm core structure, and asymmetrical eyewall convection. The improvements conferred by the assimilating the total observations in the analysis and forecasting of TC tracks and intensities are mainly contributed by the inner‐core observations. This is probably because: (a) the model has a higher deficiency in simulating the TC inner‐core structure, which leads to a larger background deviation relative to the observations and the assimilation increment contributed by the inner‐core observations; (b) as determined by the stronger nonlinearity of the physical processes, the ensemble spread (i.e., model uncertainty) around the inner core is larger, resulting in a more distinct impact of data assimilation. The significant improvements in the inner‐core observations are further consolidated by analyses of two other TCs of different sizes and intensities, in which the number of inner‐core observations accounts for only 1/3 of the total on average. The TC inner‐core area should be a major consideration of future TC observation projects. Plain Language Summary: This study analyzes the individual impact and contribution of the radar radial wind observation within the inner‐ and outer‐cores of tropical cyclones (TCs) to its analysis and forecasting. Regarding the case study of TC Mujigae (2015), the inner‐core observations contribute more to enhancing tangential wind and updraft near the eyewall as well as correcting the TC size, warm core structure, and asymmetrical eyewall convection. The improvements of radar data assimilation to TC track and intensity are found to mainly come from the assimilation of inner‐core observations. This may come from two aspects: (a) the model has a higher deficiency in simulating the precise structure of TC inner‐core, which leads to a larger assimilation increment by inner‐core observation. (b) the physical processes of the TC inner‐core region have a stronger nonlinearity, which makes a larger uncertainty in the model background, resulting in a more distinct impact of data assimilation. These results are further validated by two other TCs of different sizes and intensities. The TC inner‐core area should be a major consideration of future TC observation projects. Key Points: The impacts and contributions of assimilating radar radial winds within tropical cyclone (TC) inner‐ and outer‐core regions are analyzed for three TC casesThe improvements on TC track and intensity are primarily due to the assimilation of inner‐core observationsThe dominant impact of inner‐core data is due to the large model deficiency and strong nonlinearity of TC inner‐core dynamic processes [ABSTRACT FROM AUTHOR]

Micro-Doppler signatures obtained from the Doppler radar are generally used for human activity classification. However, if the angle between the direction of motion and radar antenna broadside is greater than 60°, the micro-Doppler signatures generated by the radial motion of human body reduce significantly, thereby degrading the performance of the classification algorithm. For the accurate classification of different human activities irrespective of trajectory, we propose a new algorithm based on dual micro-motion signatures, namely, the micro-Doppler and interferometric micro-motion signatures, using an interferometric radar. First, the motion of different parts of the human body is simulated using motion capture (MOCAP) data, which is further utilized for radar echo signal generation. Second, time-varying Doppler and interferometric spectrograms obtained from time-frequency analysis of a single Doppler receiver and interferometric output data, respectively, are fed as input to the deep convolutional neural network (DCNN) for feature extraction and the training/testing process. The performance of the proposed algorithm is analyzed and compared with a micro-Doppler signatures-based classifier. Results show that a dual micro-motion-based DCNN classifier using an interferometric radar is capable of classifying different human activities with an accuracy level of 98%, where Doppler signatures diminish considerably, providing insufficient information for classification. Verification of the proposed classification algorithm based on dual micro-motion signatures is also performed using a real radar test dataset of different human walking patterns, and a classification accuracy level of approximately 90% is achieved. [ABSTRACT FROM AUTHOR]

This paper proposes a corner reflector and Luneburg ball reflector group. The omnidirectional radar cross-sectional (RCS) distribution characteristics of a fighter are simulated using the sharp and smooth RCS distribution features of the corner and the Luneburg ball reflectors, respectively. A new type of Doppler signal generation principle is proposed to design a Doppler frequency simulator to transmit frequency signals by connecting in parallel with the metal layer of the corner reflector and the Luneburg sphere ball, and then transmit through their other end. The existing radar target aircraft cannot simulate the RCS and speed of the targets that are less than 0.005 m² by enhancing the echo intensity of the target location, which makes it impossible for the military to conduct practice drills and evaluate the effectiveness of the air defense systems. The experimental results show that the Doppler frequency simulator successfully simulates the target speed of 0-80 km/h and when the speed is greater than 20 km/h, the error of the simulation frequency is less than 1.5%. The proposed method can provide guidance and a theoretical basis for simulating the speed of various types of aircraft in future work. [ABSTRACT FROM AUTHOR]

The paper is devoted to the problem of estimating marine current velocity from microwave radar data, one of the important tasks of sea remote sensing. We present some results of simultaneous measurements of radar scatterers velocities and sea current and wind velocities. Radar scatterers velocities were measured using a dual-polarized (VV/HH) Doppler radar operating in S/C/X bands. The experiments were carried out in the coastal zone of the Black Sea at moderate incidence angles (30–70 degrees). It was obtained that the subsurface current velocity (current in the upper layer of ten centimeters) retrieved from the Bragg component of the radar return can be used to estimate changes in marine current (a part of the sea current that is not related to the wind) at constant wind speed. The subsurface current velocity is found as a vector sum of the current velocity measured at a depth of 1 m and the wind component equal to 1–3% of the wind speed. Possibilities of estimating the current velocity from VV/HH/PD data are analyzed. [ABSTRACT FROM AUTHOR]

Coincident radar data with Doppler radar measurements at X, Ku, Ka, and W bands on the NASA ER-2 aircraft overflying the NASA P-3 aircraft acquiring in situ microphysical measurements are used to characterize the relationship between radar measurements and ice microphysical properties. The data were obtained from the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS). Direct measurements of the condensed water content and coincident Doppler radar measurements were acquired, facilitating improved estimates of ice particle mass, a variable that is an underlying factor for calculating and therefore retrieving the radar reflectivity Ze, median mass diameter Dm, particle terminal velocity, and snowfall rate S. The relationship between the measured ice water content (IWC) and that calculated from the particle size distributions (PSDs) using relationships developed in earlier studies, and between the calculated and measured radar reflectivity at the four radar wavelengths, are quantified. Relationships are derived between the measured IWC and properties of the PSD, Dm, Ze at the four radar wavelengths, and the dual-wavelength ratio. Because IWC and Ze are measured directly, the coefficients in the mass--dimensional relationship that best match both the IWC and Ze are derived. The relationships developed here, and the mass--dimensional relationship that uses both the measured IWC and Ze to find a best match for both variables, can be used in studies that characterize the properties of wintertime snow clouds. [ABSTRACT FROM AUTHOR]

The use of meteorological radars to study snowfall microphysical properties and processes is well established, in particular via a few distinct techniques: the use of radar polarimetry, of multi-frequency radar measurements, and of the radar Doppler spectra. We propose a novel approach to retrieve snowfall properties by combining the latter two techniques, while relaxing some assumptions on, e.g., beam alignment and non-turbulent atmosphere. The method relies on a two-step deep-learning framework inspired from data compression techniques: an encoder model maps a high-dimensional signal to a low-dimensional latent space, while the decoder reconstructs the original signal from this latent space. Here, Doppler spectrograms at two frequencies constitute the high-dimensional input, while the latent features are constrained to represent the snowfall properties of interest. The decoder network is first trained to emulate Doppler spectra from a set of microphysical variables, using simulations from the Passive and Active Microwave radiative TRAnsfer model (PAMTRA) as training data. In a second step, the encoder network learns the inverse mapping, from real measured dual-frequency spectrograms to the microphysical latent space; in doing so, it leverages with a convolutional structure the spatial consistency of the measurements to mitigate the ill-posedness of the problem. The method was implemented on X- and W-band data from the ICE GENESIS campaign that took place in the Swiss Jura Mountains in January 2021. An in-depth assessment of the retrieval accuracy was performed through comparisons with colocated aircraft in situ measurements collected during three precipitation events. The agreement is overall good and opens up possibilities for acute characterization of snowfall microphysics on larger datasets. A discussion of the sensitivity and limitations of the method is also conducted. The main contribution of this work is, on the one hand, the theoretical framework itself, which can be applied to other remote-sensing retrieval applications and is thus possibly of interest to a broad audience across atmospheric sciences. On the other hand, the seven retrieved microphysical descriptors provide relevant insights into snowfall processes. [ABSTRACT FROM AUTHOR]

The rescaling of rainfall requires measurements of rainfall rates over many dimensions. This paper develops one approach using 10 m vertical spatial observations of the Doppler spectra of falling rain every 10 s over intervals varying from 15 up to 41 min in two different locations and in two different years using two different micro-rain radars (MRR). The transformation of the temporal domain into spatial observations uses the Taylor "frozen" turbulence hypothesis to estimate an average advection speed over an entire observation interval. Thus, when no other advection estimates are possible, this paper offers a new approach for estimating the appropriate frozen turbulence advection speed by minimizing power spectral differences between the ensemble of purely spatial radial power spectra observed at all times in the vertical and those using the ensemble of temporal spectra at all heights to yield statistically reliable scaling relations. Thus, it is likely that MRR and other vertically pointing Doppler radars may often help to obviate the need for expensive and immobile large networks of instruments in order to determine such scaling relations but not the need of those radars for surveillance. [ABSTRACT FROM AUTHOR]

Optimizing radar observation strategies is one of the most important considerations in pre-field campaign periods. This is especially true for isolated convective clouds that typically evolve faster than the observations captured by operational radar networks. This study investigates uncertainties in radar observations of the evolution of the microphysical and dynamical properties of isolated deep convective clouds developing in clean and polluted environments. It aims to optimize the radar observation strategy for deep convection through the use of high-spatiotemporal cloud-resolving model simulations, which resolve the evolution of individual convective cells every 1 min, coupled with a radar simulator and a cell tracking algorithm. The radar simulation settings are based on the Tracking Aerosol Convection Interactions ExpeRiment (TRACER) and Experiment of Sea Breeze Convection, Aerosols, Precipitation and Environment (ESCAPE) field campaigns held in the Houston, TX, area but are generalizable to other field campaigns focusing on isolated deep convection. Our analysis produces the following four outcomes. First, a 5–7 m s -1 median difference in maximum updrafts of tracked cells is shown between the clean and polluted simulations in the early stages of the cloud lifetimes. This demonstrates the importance of obtaining accurate estimates of vertical velocity from observations if aerosol impacts are to be properly resolved. Second, tracking of individual cells and using vertical cross section scanning every minute capture the evolution of precipitation particle number concentration and size represented by polarimetric observables better than the operational radar observations that update the volume scan every 5 min. This approach also improves multi-Doppler radar updraft retrievals above 5 km above ground level for regions with updraft velocities greater than 10 m s -1. Third, we propose an optimized strategy composed of cell tracking by quick (1–2 min) vertical cross section scans from more than one radar in addition to the operational volume scans. We also propose the use of a single-RHI (range height indicator) updraft retrieval technique for cells close to the radars, for which multi-Doppler radar retrievals are still challenging. Finally, increasing the number of deep convective cells sampled by such observations better represents the median maximum updraft evolution with sample sizes of more than 10 deep cells, which decreases the error associated with sampling the true population to less than 3 m s -1. [ABSTRACT FROM AUTHOR]

The ambiguity problem of targets in Doppler beam sharpening (DBS) with forward-looking radar is considered. While DBS is proposed earlier to improve the angular resolution of the radar while keeping the antenna aperture size limited, such a solution suffers from ambiguities in the case of targets positioned symmetrically with respect to the platform movement. To address this problem, an approach named unambiguous Doppler-based forward-looking multiple-input multiple-output (MIMO) radar beam sharpening scan (UDFMBSC) is proposed, based on the combination of MIMO processing and DBS. The performance of the proposed method is compared to existing approaches using simulated data with point-like and extended targets. The method is successfully verified using experimental data. [ABSTRACT FROM AUTHOR]

Linked to the increasing availability of datasets for radar-based human activity recognition (HAR), in this Student Highlights contribution, we report on a classification project that a group of 23 graduate students performed at TU Delft. The students were asked to work in groups of 2–3 members and to use the publicly available University of Glasgow dataset to develop the best classification pipeline as possible. This involved development and justification of both choices for the preprocessing techniques on the radar data (e.g., time–frequency distributions and cleaning of the signatures), and for the classification algorithms (e.g., the type of the algorithm, the hyperparameters' selection, the training–validation–testing split). While this student activity was performed at a small scale and with educational rather than research aims, we are happy to report it to the AESS readership, as we believe that such initiatives with open datasets sharing and classification algorithm benchmarking are beneficial for the wider radar research community. Furthermore, a list of publicly available datasets for radar-based HAR that can be used for similar initiatives is also reported in this article. [ABSTRACT FROM AUTHOR]

This work describes the implementation of an updated radar reflectivity assimilation scheme with the three-dimensional variational (3D-Var) system of Weather Research and Forecast (WRF). The updated scheme, instead of the original scheme assuming the relative humidity to a fixed value where radar reflectivity is higher than a threshold, assimilates pseudo water vapor retrieved by the Bayesian method, which would be consistent with clouds/precipitations provided by the model in theory. To verify the effect of the updated scheme to the improvement of precipitation simulation, a convective case in Wenquan County and the continuous monthly simulation with contrasting experiments in Xinjiang were performed. The test of single reflectivity observation demonstrates that the water vapor retrieved by the Bayesian method is consistent with the meteorological situation around. In the convective case, both the updated and original scheme results show that the assimilation of pseudo water vapor can adjust to the environmental conditions of water vapor and temperature. This can improve the hourly precipitation forecast skill more than the contrasting experiment, which was designed to only assimilate conventional observations and radar radial velocity data. In the continuous monthly experiments, the updated scheme reveals that the analysis of water vapor is more reasonable, and obtains a better precipitation forecast skill for 6 h accumulated precipitation than the contrasting experiments. [ABSTRACT FROM AUTHOR]

Radar-based human activity recognition (HAR) has been applied in many fields such as human–computer interaction, smart surveillance, and health assessment. With the development of deep learning, many deep-learning models have been proposed in radar-based HAR to achieve high classification accuracy. Inevitably, new human activity classes appear continuously, and this requires the deep-learning models to efficiently learn novel categories from only a few data samples while at the same time maintaining high accuracy on the initial human activities on which they were trained. Therefore, this article proposes a category-extensible HAR model based on few-shot learning. The classification weight of the new human activity category is quickly generated through the feature vector extracted by the trained feature extractor and the classification weight of the most similar original category. To solve the mismatching of weight value intervals, we also employ the cosine similarity-based classifier. Furthermore, we adopted the large margin in the softmax cross-entropy (LMSC) loss function to improve the model’s performance and depthwise separable convolution to reduce the computation of the model. The experimental results show that the model can quickly adapt to the new human activity classification task and did not sacrifice the performance on category classification of the original activities. With relatively low computational complexity, our method achieves 81.99% of accuracy when each extended category has only one sample to generate the weights and 91.60% when each of them has only five samples in the task of a three-way. [ABSTRACT FROM AUTHOR]

The Micro Rain Radar PRO (MRR-PRO) is a K-band Doppler weather radar, using frequency-modulated continuous-wave (FMCW) signals, developed by Metek Meteorologische Messtechnik GmbH (Metek) as a successor to the MRR-2. Benefiting from four datasets collected during two field campaigns in Antarctica and Switzerland, we developed a processing library for snowfall measurements named ERUO (Enhancement and Reconstruction of the spectrUm for the MRR-PRO), with a twofold objective. Firstly, the proposed method addresses a series of issues plaguing the radar variables, including interference lines and power drops at the extremes of the Doppler spectrum. Secondly, the algorithm aims to improve the quality of the final variables by lowering the minimum detectable equivalent attenuated reflectivity factor and extending the valid Doppler velocity range through dealiasing. The performance of the algorithm has been tested against the measurements of a co-located W-band Doppler radar. Information from a close-by X-band Doppler dual-polarization radar has been used to exclude unsuitable radar volumes from the comparison. Particular attention has been dedicated to verifying the estimation of the meteorological signal in the spectra covered by interferences. [ABSTRACT FROM AUTHOR]