Radar Range Research Articles

Design of a Two-Stage Continuous Fall Detection System Using Multiframe Radar Range–Doppler Maps

Design of a Two-Stage Continuous Fall Detection System Using Multiframe Radar Range–Doppler Maps

Spatial evaluation rainfall estimation on weather radar using Marshall–Palmer reflectivity–rainfall rate in Banten

Based on data from the National Disaster Management Agency (Ind.: Badan Nasional Penanggulangan Bencana – BNPB), throughout 2022, more than 91% of disaster events were hydrometeorological disasters, with floods at 43% and landslides at 17%. One of the factors for floods and landslides is high rainfall intensity. Automatic rain gauge (ARG) is a rainfall observation instrument that can accurately measure rainfall at observation points. However, it has problems such as communication systems that cause delays in data transmission, low instrument density, and inability to cover a wide spatial area, which can affect the accuracy of rainfall information. Weather radar is a remote sensing instrument that can estimate rainfall spatially so that weather radar observations can reach areas of the region that do not have ARG. However, before being used as rainfall information, estimation rainfall needs to be evaluated or calibrated. Evaluation of rainfall estimation on weather radar to ARG in Banten at a 30– 120 km distance range, shows a coefficient of determination above 0.8. Based on the studies that have been conducted, increase of root mean square error (RMSE) is due to influence of radar observation range and observation distance on ARG. Adjustment of rainfall estimation improves the accuracy of rainfall estimation. Adjusting rainfall estimation can reduce RMSE by 50%.

Typhoon Early Warning and Monitoring Based on the Comprehensive Characteristics of Oceanic and Ionospheric Echoes from HFSWR: The Case of Typhoon Muifa

As devastating natural disasters, typhoons pose a tremendous threat to human society, making effective typhoon early warning and monitoring crucial. To address this challenge, High Frequency Surface Wave Radar (HFSWR), which can observe oceanic parameters such as typhoon wind fields in real time and even capture the dynamic changes in the ionosphere, has become an effective tool for typhoon monitoring. This paper investigates the interaction mechanisms about Typhoon-Acoustic Gravity Waves (AGWs)-Ionosphere, as well as Typhoon-Ocean Waves for HFSWR, and simulates these interaction processes within HFSWR. Then a typhoon early warning and monitoring scheme for HFSWR has been proposed: In the first stage, the S-shaped ionospheric disturbances observed by HFSWR are utilized as precursor signals for early typhoon warnings. In addition, the second stage involves analyzing changes in first-order oceanic echo spectral peak ratio to pinpoint when the typhoon eye enters the radar detection range, thus initiating the typhoon monitoring phase. Subsequently, the measured data from HFSWR collected during Typhoon “Muifa” (2212) in conjunction with the proposed scheme are evaluated in detail. The results indicate that AGWs generated by typhoons can propagate into non-typhoon areas within the detection range, causing S- shaped ionospheric disturbances and providing approximately 6 h of early warning. At around 8:05 (UTC+8), an increasing trend in the first-order spectral peak ratio was noted, indicating the entry of the typhoon eye into the detection range, which closely aligns with the official typhoon path and marks the transition to the monitoring phase. The proposed scheme is expected to enhance the capability for typhoon early warning and real-time monitoring in specific sea areas and mitigate the risks associated with typhoon-related disasters.

Coseismic surface ruptures of 20 strike-slip earthquakes measured from geodetic imaging data

Observations of coseismic surface ruptures are widely used for examining faulting mechanics and kinematics, and in fault displacement hazard analysis. Here, first, we provide new remote sensing observations of 20 historic surface rupturing continental strike-slip earthquakes (6.1 ≤ Mw≤ 7.9). To measure the near-field surface deformation pattern, we have processed a range of raw radar and optical images from satellite and aerial platforms with pixel tracking techniques, and in two cases using standard interferometric synthetic aperture radar (InSAR). We provide a range of observations, including surface displacement maps that constrain at least the horizontal component of surface motion (2D), and for three events the full 3D components. Second, we provide strain invariants for each event, which includes the finite dilatational strain, strain magnitudes, the vorticity, and maximum shear strain. Third, we provide a total of 134 surface fault rupture traces that are mapped manually from the displacement maps. Finally, we provide 2648 measurements of coseismic horizontal fault-parallel surface fault slip that are measured objectively using swath profiles with a function fitting method that we have developed.

Optimal radar ranging pulse to resolve two reflectors

Previous work established fundamental bounds on subwavelength resolution for the radar range resolution problem, called superradar []. In this work, we identify the optimal waveforms for distinguishing the range resolution between two reflectors of identical strength, leveraging results in quantum metrology. We discuss both the unnormalized optimal waveform as well as the best square-integrable pulse and their variants. Using orthogonal function theory, we give an explicit algorithm to optimize the wave pulse in finite time to have the best performance. We also explore range resolution estimation with unnormalized waveforms with multiparameter methods to also independently estimate loss and time of arrival. These results are consistent with the earlier single parameter approach of range resolution only and give deeper insight into the ranging estimation problem. Experimental results are presented using radio pulse reflections inside coaxial cables, showing robust range resolution smaller than a tenth of the inverse bandlimit, with uncertainties close to the derived Cramér-Rao bound. Published by the American Physical Society 2024

Limited Sample Radar HRRP Recognition Using FWA-GAN

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

Data‐driven target localization using adaptive radar processing and convolutional neural networks

AbstractLeveraging the advanced functionalities of modern radio frequency (RF) modeling and simulation tools, specifically designed for adaptive radar processing applications, this paper presents a data‐driven approach to improve accuracy in radar target localization post adaptive radar detection. To this end, we generate a large number of radar returns by randomly placing targets of variable strengths in a predefined area, using RFView®, a high‐fidelity, site‐specific, RF modeling & simulation tool. We produce heatmap tensors from the radar returns, in range, azimuth [and Doppler], of the normalized adaptive matched filter (NAMF) test statistic. We then train a regression convolutional neural network (CNN) to estimate target locations from these heatmap tensors, and we compare the target localization accuracy of this approach with that of peak‐finding and local search methods. This empirical study shows that our regression CNN achieves a considerable improvement in target location estimation accuracy. The regression CNN offers significant gains and reasonable accuracy even at signal‐to‐clutter‐plus‐noise ratio (SCNR) regimes that are close to the breakdown threshold SCNR of the NAMF. We also study the robustness of our trained CNN to mismatches in the radar data, where the CNN is tested on heatmap tensors collected from areas that it was not trained on. We show that our CNN can be made robust to mismatches in the radar data through few‐shot learning, using a relatively small number of new training samples.

Photonic stepped-frequency radar with 150-m unambiguous detection and centimeter range resolution.

Photonic stepped-frequency radars based on optical frequency-shifting modulation have shown attractive properties such as wide bandwidth, centimeter range resolution, inherent frequency-time linearity with low spectrum spurs, and reduced system complexity. However, existing approaches typically exhibit meter- or centimeter-level radar range ambiguity, inversely proportional to the frequency step, due to the large frequency shift determined by acousto-optic or electro-optic (EO) modulators. Here, we overcome this limitation by injecting a narrowband, stepped-frequency signal into an optical frequency-shifting fiber cavity to achieve, for the first time, to our knowledge, a broadband photonic stepped-frequency radar with 150-m unambiguous detection and centimeter range resolution, surpassing the reported photonic- and electronic-based counterparts. The demonstrated approach effectively resolves the trade-off between ambiguity range and shifting frequency while maintaining the signal quality and bandwidth, bringing its practicality into reach for outdoor applications.

Estimating Uncertainties from Dual-Doppler Radar Measurements of Onshore Wind Plants Using LES

Towards the ongoing work of improving the capability of flow modeling within and around wind plants, an onshore model validation benchmark campaign is underway based on a field experiment involving multiple wind plants in Oklahoma, U.S.A. Dual-Doppler radar is being leveraged to provide flowfield information for the benchmarking owing to the unparalleled capability of such radar to capture minute-by-minute horizontal wind fields over a scale of tens of kilometers. However, dual-Doppler radar exhibits sampling artifacts that must be considered during model validation, and these are due to probe-volume averaging, coarse azimuthal/elevational resolution, non-ideal stereo angles, and coarse temporal sampling. Such sources of error in radar-reconstructed flowfields can be quantified using virtual radar sampling in the high-fidelity simulation environment (i.e., large-eddy simulation (LES)) where the true velocity field is known with confidence, and this is the uncertainty quantification approach adopted in this article. We leverage a virtual radar tool designed to replicate the specific sampling strategy of the X-band dual-Doppler instrument installed in the field campaign. This tool is featured in LES of an expansive 100 km by 100 km region of Oklahoma including hundreds of wind turbines modeled as actuator disks. In agreement with the sampling principles of radar, the results show that large-scale flow structures are qualitatively well-resolved by the instrument, though more simulation time and analysis are needed to determine the accuracy of the radar’s integral lengthscale estimates. At the turbine scale, the radar struggles to capture all of the features of the turbine wakes. The process of probe-volume averaging, as well as the subsequent interpolation to a Cartesian grid, biases the reconstruction of the peak near-wake deficit by an average of 2 m/s, or around 20% of the freestream velocity. Errors in this quantity of interest, as well as one characterizing the magnitude of the free-flow wind speed around the wakes, are found to be sensitive to the radar beam-crossing angle and beam range.

Cylindrical Scattered Field Transformation Applicable for Differences in the Radar Ranges of Transmitter and Receiver—Far-Field Prediction

Cylindrical Scattered Field Transformation Applicable for Differences in the Radar Ranges of Transmitter and Receiver—Far-Field Prediction

Evaluation of Lateral Radar Positioning for Vital Sign Monitoring: An Empirical Study.

Vital sign monitoring is dominated by precise but costly contact-based sensors. Contactless devices such as radars provide a promising alternative. In this article, the effects of lateral radar positions on breathing and heartbeat extraction are evaluated based on a sleep study. A lateral radar position is a radar placement from which multiple human body zones are mapped onto different radar range sections. These body zones can be used to extract breathing and heartbeat motions independently from one another via these different range sections. Radars were positioned above the bed as a conventional approach and on a bedside table as well as at the foot end of the bed as lateral positions. These positions were evaluated based on six nights of sleep collected from healthy volunteers with polysomnography (PSG) as a reference system. For breathing extraction, comparable results were observed for all three radar positions. For heartbeat extraction, a higher level of agreement between the radar foot end position and the PSG was found. An example of the distinction between thoracic and abdominal breathing using a lateral radar position is shown. Lateral radar positions could lead to a more detailed analysis of movements along the body, with the potential for diagnostic applications.

Radar Range–Doppler Flow: A Radar Signal Processing Technique to Enhance Radar Target Classification

Radar Range–Doppler Flow: A Radar Signal Processing Technique to Enhance Radar Target Classification

Radar near-field sensing using metasurface for biomedical applications

Metasurfaces, promising technology exemplified by their precise manipulation of incident wave properties and exquisite control over electromagnetic field propagation, offer unparalleled benefits when integrated into radar systems, providing higher resolution and increased sensitivity. Here, we introduce a metasurface-enhanced millimeter-wave radar system for advanced near-field bio-sensing, underscoring its adaptability to the skin-device interface, and heightened diagnostic precision in non-invasive healthcare monitoring. The low-profile planar metasurface, featuring a phase-synthesized array for near-field impedance matching, integrates with radar antennas to concentrate absorbed power density within the skin medium while simultaneously improving the received power level, thereby enhancing sensor signal-to-noise ratio. Measurement verification employs a phantom with material properties resembling human skin within the radar frequency range of 58 to 63 GHz. Results demonstrate a notable increase of over 11 dB in near-field Poynting power density within the phantom model, while radar signal processing analysis indicates a commensurate improvement in signal-to-noise ratio, thus facilitating enhanced sensing in biomedical applications.

Integrated Design and Prototyping of a 77 GHz Automotive Medium Range Radar Into Car Rear Lamp

Integrated Design and Prototyping of a 77 GHz Automotive Medium Range Radar Into Car Rear Lamp

Airborne distributed array position calibration using sparse spatial-frequency accompanying flight MISO array

Airborne coherent distributed array radar (DCAR) can greatly improve radar detection range and angle resolution. However, the time-variant property of airborne DCAR elements’ position strongly limits the application in real situations. In this paper, we developed a high-accuracy position estimation method for airborne DCAR systems with position errors using a single accompanying flight (AF) array. The basic principle is using a small array with a spatial sparse configuration to transmit a sparse-frequency multiple input single output (MISO) signal. Then airborne DCAR elements receive the signals and perform multi-channel demodulation and parameter estimation processing to obtain their distance and angle information relative to the AF array. The presented method estimates the position of all DACR elements with the single same array and thus various inaccordance between different transmit sources can be avoided. By using a spatial-frequency sparse array, the system complexity of the accompanying flight platform can be greatly reduced. Our method can obtain position estimates that are smaller than the operating wavelength, which ensures that sufficient performance can be obtained from coherent processing. Simulation results are given to demonstrate the effectiveness of the proposed method.

Removal of Synthetic Aperture Radar Range Ambiguity by Observing Adjacent Regions

Removal of Synthetic Aperture Radar Range Ambiguity by Observing Adjacent Regions

Проблема обнаружения аномальных волн на воде навигационными радиолокаторами. 2. Решение уравнения радиолокации

Simple options for the effective scattering area of anomalous waves are considered in the geometric optics approximation for two types of targets: a single wave and a white wall. The radar equation has been solved to find the maximum radar range for such targets, taking into account the influence of significant wave heights on the effective scattering area of anomalous waves and taking into account interference due to the scattering of electromagnetic waves on sea waves. Conditions are proposed for the lifetime and height of anomalous waves necessary for observing these waves with standard ship radars.

ACCURACY ANALYSIS OF AIRCRAFT POSITIONING USING NAVIGATIONAL DATA FROM AVIA-W RADAR

The paper presents an analysis of the accuracy of determination of parameters of the position of aircraft using data from the AVIA-W radar. In the first place, the authors determined the position of the aircraft as well as the range and azimuth parameters by the AVIA-W radar, located in Dęblin. This was followed by a determination of the absolute position error of the aircraft and the determination of the range and azimuth measurement error by the AVIA-W radar. The research test was carried out using a Diamond DA40 NG aircraft on which a GPS satellite receiver was mounted in order to determine the flight reference position. In addition, the range and azimuth measurements for the aircraft were acquired from the AVIA-W radar. Navigational calculations were conducted for polar and rectangular planar coordinates. Based on the performed research, the azimuth error was found to be –1.4o, while the radar range measurement error was equal to –0.04 km. The conducted research is experimental in its character. In the future it will be repeated and extended to the GCA-2000 radar, which is also located at Dęblin military airfield.

Spectral-Efficient Frequency-Division Photonic Millimeter-Wave Integrated Sensing and Communication System Using Improved Sparse LFM Sub-Bands Fusion

Scheduling radar and communication functions in a frequency-division multiplexing (FDM) fashion is a classic solution to achieve mutual-interference-free integrated sensing and communication (ISAC) system. However, it is haunted by poor spectral efficiency (SE) in comparison to the spectrum-sharing scheme. In this paper, we propose a novel photonics millimeter-wave ISAC system reaping the FDM scheme while offering inspirations for significantly enhancing SE using super-resolution techniques. Using the coherent fusion processing (CFP) of sparse sub-band linear frequency modulated (LFM) radar signals, a small fraction of the total bandwidth can enable a full-band-equivalent radar range resolution and more spectrum resources can be allocated for communications, leading to an ultrahigh SE for ISAC system. Moreover, an improved sparse sub-band fusion algorithm is developed based on particle swarm optimization to enhance the accuracy of synthesized radar range profiles. In experiments, a 60-GHz super-resolution FDM ISAC system using photonics-assisted millimeter-wave signal generation and dual-parallel coherent de-chirping processing is established, wherein the idle spectrum between two widely-spaced LFM sub-bands is loaded with broadband orthogonal frequency division multiplexing (OFDM) communication signals. Our system can simultaneously achieve up to 18-Gbit/s communication data rate and a radar range resolution as high as 2.14 cm, by exploring the globally unlicensed 7-GHz spectrum around 60-GHz with 4.5-GHz communication and 2-GHz radar bandwidths. In addition, a 0.97-cm radar range resolution corresponding to 16-GHz equivalent bandwidth (EB) is experimentally obtained by using two 1.5-GHz LFM sub-bands. This renders only ∼9.4% EB loss (3 GHz/32 GHz), without significant performance tradeoff between sensing and communications.

THE METHOD OF ADJUSTING THE MULTI-FREQUENCY PROBING RADAR SIGNAL PARAMETERS AND THE TROPOSPHERIC RADIO WAVEGUIDE CHARACTERISTICS

Peculiarities of air circulation in the coastal zone of large reservoirs (large lakes or inland seas such as the Black and Azov Seas) can lead to a significant departure from the standard spatial distribution of meteorological and, accordingly, radiophysical characteristics of air.
 As a result, it is possible for regions of space with an abnormally low attenuation level of radio waves of certain frequency ranges to appear – tropospheric radio waveguides (TWG). The appearance of TWG leads to multi-beam propagation of radio waves and fluctuations of radar signals. Fluctuations of the radar signal lead to a decrease in the range of detection of aerial objects by radars. For single-frequency radar signals, it is only possible to estimate the degree of distortion and the amount of radar detection range loss. A paradoxical situation arises when, due to TWG, it is possible to increase the range of detection of aerial objects, but due to the distortion of the radar signal in TWG, the detection performance decreases. For multi-frequency signals, it is potentially possible to adjust the parameters of the radar signal with the parameters of the radar channel (in this case, the tropospheric radio waveguide).
 The adjustable parameters of a multifrequency signal include: the number of frequency components; bandwidth of the frequency component; average frequency of the frequency component; signal duration; period.
 The characteristics of TWG, on which the parameters of the multifrequency signal depend, include: the lower transmission frequency of TWG; the upper transmission frequency of TWG; the delay time of the rays in the TWG.
 The bandwidth and beam delay values can be obtained directly by emitting and receiving a test multi- frequency pilot signal, but this method requires the presence of a reference reflector in space, which is difficult to implement in practice.
 The electrophysical characteristics of the tropospheric waveguide can be determined from the data of meteorological observations. The method developed in the article allows, based on the known characteristics of the tropospheric waveguide, to determine and adjust the parameters of a multi-frequency signal, which reduces the fluctuations of the sounding signal and increases the aerial objects detection rang.