Search

An absorptive coding metasurface (ACM) is proposed to achieve radar cross section (RCS) reduction in this paper. In the design progress of the ACM, two different lossy coding elements are proposed at first, which can both be regarded as a composite composed of a two-layer resistive frequency selective surface (RFSS) and a polarization conversion metasurface (PCM). The two-layer RFSS has a certain wave-absorbing property due to ohmic loss. In addition, the PCM can achieve ultra-wideband linear polarization conversion, and the polarization-converted reflected waves in the two coding elements under the same incidence will differ by nearly 180° in phase because the sub-unit-cell structures in them are perpendicular to each other. Thus, based on the two lossy coding elements, the ACM is proposed, which can achieve ultra-wideband RCS reduction due to absorption and phase cancelation. Numerical simulations demonstrate that the RCS of the ACM under normal incidence can be reduced more than 10 dB in the ultra-wide frequency band from 6.9 to 41.2 GHz with a relative bandwidth of 142.6%. Moreover, it has the advantages of polarization-insensitivity and wide incident angle. Finally, one effective experimental verification is carried out, and a reasonable agreement is observed between the simulation and experimental results. [ABSTRACT FROM AUTHOR]

Contrasting the X-band phased array radar (XPAR) with the conventional S-Band dual-polarization mechanical scanning radar (SMSR), the XPAR offers superior temporal and spatial resolution, enabling a more refined depiction of the internal dynamics within convective systems. While both SMSR and XPAR data are extensively used in monitoring and alerting for severe convective weather, their comparative application in numerical weather prediction through data assimilation remains a relatively unexplored area. This study harnesses the Weather Research and Forecasting Model (WRF) and its data assimilation system (WRFDA) to integrate radial velocity and reflectivity from the Guangzhou SMSR and nine XPARs across Guangdong Province. Utilizing a three-dimensional variational approach at a 1 km convective-scale grid, the assimilated data are applied to forecast a rainstorm event in the Pearl River Delta (PRD) on 6 June 2022. Through a comparative analysis of the results from assimilating SMSR and XPAR data, it was observed that the assimilation of SMSR data led to more extensive adjustments in the lower- and middle-level wind fields compared to XPAR data assimilation. This resulted in an enlarged convergence area at lower levels, prompting an overdevelopment of convective systems and an excessive concentration of internal hydrometeor particles, which in turn led to spurious precipitation forecasts. However, the sequential assimilation of both SMSR and XPAR data effectively reduced the excessive adjustments in the wind fields that were evident when only SMSR data were used. This approach diminished the generation of false echoes and enhanced the precision of quantitative precipitation forecasts. Additionally, the lower spectral width of XPAR data indicates its superior detection accuracy. Assimilating XPAR data alone yields more reasonable adjustments to the low- to middle-level wind fields, leading to the formation of small-to-medium-scale horizontal convergence lines in the lower levels of the analysis field. This enhancement significantly improves the model's forecasts of composite reflectivity and radar echoes, aligning them more closely with actual observations. Consequently, the Threat Score (TS) and Equitable Threat Score (ETS) for heavy-rain forecasts (>10 mm/h) over the next 5 h are markedly enhanced. This study underscores the necessity of incorporating XPAR data assimilation in numerical weather prediction practices and lays the groundwork for the future joint assimilation of SMSR and XPAR data. [ABSTRACT FROM AUTHOR]

Due to the scheme of fixed-platform beam-steering radar and the space of the blast furnace being subjected to harsh environmental influences, the traditional detection methods of burden surface are limited by geometric distortion, noncoherent clutter, and noise interference, which leads to an increase in the image entropy value and the equivalent number of views, makes the density distribution of burden surface show a diffuse state, and greatly affects the stability and accuracy. In this paper, a new fixed-platform beam-steering radar synthetic aperture radar imaging method (FPBS-SAR) is proposed in the sensory domain of the blast furnace environment. From the perspective of fixed-platform beam-steering radar motion characteristics, the target range–azimuth coupled distance history model under the sub-aperture is established, the azimuthal Doppler variation characteristics of the fixed-platform beam-steering process are analyzed, and the compensation function of the transform domain for geometric disturbance correction is proposed. For noncoherent noise suppression in blast furnaces, the trimmed geometric mean-order-likelihood CFAR method is proposed to take into account the information of burden surface and clutter suppression. To verify the method, point target simulation and imaging for the industrial field measurement data are carried out. The results indicate that geometric distortion is well eliminated, the image entropy value and the equivalent number of views have decreased, and noncoherent noise in blast furnaces is suppressed. [ABSTRACT FROM AUTHOR]

Forward‐looking radar imaging is a top priority due to a variety of applications. An airborne forward‐looking radar imaging approach via a modified propagator method (MPM) based on the planar phased array is proposed. It can obtain a better focusing effect without range migration correction. Through the MPM algorithm, the azimuth resolution can be enhanced greatly. Compared with the conventional synthetic aperture radar imaging algorithm, the left‐right ambiguity process can be avoided by generating two‐dimensional spatial spectra. Furthermore, the imaging results of different numbers of elements are given to provide an assessment of the imaging performance. Additionally, the authors proposed to apply the scanning planar phased array configuration to airborne forward‐looking two‐dimensional imaging for the first time. The validity of the method is proven by experiments. The image is more focused. Furthermore, the results of the experiments verify that the proposed method is fit for high‐speed situation and aircraft height variation condition. [ABSTRACT FROM AUTHOR]

This paper presents exploratory work on the use of Testing with Concept Activation Vectors (TCAV) within a concept-based explanation framework to provide the explainability of millimeter-wave (MMW) radar target recognition. Given that the radar spectrum is difficult for non-domain experts to understand visually, defining concepts for radar remains a significant challenge. In response, drawing from the visual analytical experience of experts, some basic concepts based on brightness, striping, size, and shape are adopted in this paper. However, the simplicity of basic concept definitions sometimes leads to vague correlations with recognition targets and significant variability among individuals, limiting their adaptability to specific tasks. To address these issues, this study proposes a Basic Concept-Guided Deep Embedding Clustering (BCG-DEC) method that can effectively discover task-specific composite concepts. BCG-DEC methodically analyzes the deep semantic information of radar data through four distinct stages from the perspective of concept discovery, ensuring that the concepts discovered accurately conform to the task-specific property of MMW radar target recognition. The experimental results show that the proposed method not only expands the number of concepts but also effectively solves the problem of difficulty in annotating basic concepts. In the ROD2021 MMW radar explainability experiments, the concepts proved crucial for recognizing specific categories of radar targets. [ABSTRACT FROM AUTHOR]

Weather radar is an active remote sensing device used to monitor the full lifecycle changes in severe convective weather with high spatial and temporal resolution. Effective radar calibration is a crucial foundation for ensuring the high-quality application of observational data. This paper utilizes a UAV platform equipped with a high-precision RTK system and standard metal spheres to study the principles and methods of metal sphere calibration, constructing a complete calibration process and calibration accuracy evaluation metrics. Additionally, a collocated radar comparison observation experiment was conducted for cross-validation, and metal sphere calibration tests were performed on problematic radars. The experimental results indicate the following: (1) The combined application of a high-precision RTK system and a laser range camera can provide real-time position information on the metal sphere, improving the efficiency of radar target acquisition. (2) The calibration method based on UAV-suspended metal spheres can periodically conduct the quantitative calibration of Z and ZDR, achieving calibration accuracies within 0.5 dB and 0.2 dB, respectively, and supports the qualitative inspection of key parameters such as beamwidth and pulse width. (3) During field tests, a high success rate "coarse adjustment + fine adjustment + staring" sphere-finding technique was established, based on automatic switching between RHI, PPI, and FIX scanning modes. This method directs the UAV to adjust the metal sphere to the center of the radar distance bin, reducing the impact of uneven beam filling and bin crossing, ensuring the accuracy of scattering characteristic measurements. [ABSTRACT FROM AUTHOR]

The authors investigate the performance of an original multifunction system with two in‐band full‐duplex capable joint radar, communications and security (JRCS) transceivers in the presence of an eavesdropper. The system concept can be generalised to a network of more than two JRCS transceivers and multiple eavesdroppers, despite the present study focusing on the three‐node scenario. By combining a bandlimited pseudonoise waveform with a data‐containing orthogonal frequency‐division multiplexing (OFDM) waveform, the authors are able to ensure a certain jamming‐to‐signal power ratio (JSR) at the eavesdropper, whilst ideal synchronisation ensures that jamming causes no deterioration to their own data transfer or radar sensing performance as it is possible to remove just the known pseudonoise waveform. To validate the system, the authors investigate through simulations the OFDM symbol error rates of all the receivers, radar target signal‐to‐interference‐plus‐noise power ratios as well as receiver operating characteristic curves, and eavesdropper's detection signal‐to‐noise power ratios through two‐branch receiver cross‐correlation. The results show that already a very low JSR of −20 dB can improve physical‐layer security without a significant increase in friendly symbol error rate or deterioration in radar performance. Additionally, other full‐duplex transceivers potentially occupying the same radio resources improve the secrecy even further. [ABSTRACT FROM AUTHOR]

A novel method is proposed for rail fastener detection based on millimetre‐wave (mmWave) radar, mmWave radar fastener counter (MMW‐FC), which can accurately detect and record the fasteners in real‐time as the train traverses its route. Under circumstances where GNSS signals remain unavailable for prolonged durations, precise train localisation can be accomplished by correlating the number of fasteners derived from this method with the corresponding track map. Initially, MMW‐FC utilises fast Fourier transform and adaptive beamforming to focus the energy reflected from fasteners. Subsequently, it applies an adaptive template‐matching algorithm to detect each fastener. Furthermore, by leveraging known fastener spacing and the average time for trains to pass adjacent fasteners, the Kalman filter can execute precise speed tracking, used as a speed reference when adjusting the matching template adaptively. The experimental results indicate that the proposed method can precisely count the fasteners the train encounters in diverse road and speed conditions. The fastener counter maintains the Counting Error less than 0.067%, the speed error stays below 1.8 km/h, and the maximum values of the mean absolute error and root mean square error for speed are 0.7337 and 0.9584 km/h, respectively. [ABSTRACT FROM AUTHOR]

In this letter, a novel algorithm is proposed to design symbol‐level waveform for dual‐functional radar‐communication (DFRC) systems with low range sidelobe. Different from current schemes design waveform at the block level, a phase response constraint is introduced at the symbol level to provide an additional degree of freedom to decrease the range sidelobe in the pulse compression procedure, which is highly desired in radar systems. In particular, the phase response at the target direction is constrained to be similar to the given reference phase, and the matching error between the designed beampattern and the desired one is minimized subject to the constant modulus constraint. Furthermore, to guarantee the quality of service for communication, constructive interference is exploited at the symbol level for each communication user. An alternating direction method of multipliers algorithm is also proposed to solve the resulting nonconvex optimization problem with tractable subproblems solved sufficiently by the manifold optimization and standard quadratic problem. Numerical simulation results demonstrate the performance of the proposed method. [ABSTRACT FROM AUTHOR]

Radar technology is critical for subsurface sensing applications since it enables the extraction of surface information remotely without being affected by environmental factors such as illumination and weather conditions. Hence, their usage on drones has become extremely essential as these vehicles need to accumulate data while flying over different and possibly difficult-to-access terrains regardless of visibility conditions. A frequency-modulated continuous-wave (FMCW) single input multiple output (SIMO) radar system is developed to provide near real-time radar images onboard. The system is comprised of a transceiver module, specifically designed for synthetic aperture radar (SAR) applications, to produce images in real-time. GPU-based computation is utilized to optimize processing power, while enabling range profiles to be generated and SIMO SAR images to be created during flight. The system can be easily mounted on UAV or multirotor systems. The detection and ranging performance of the system are examined by performing several experiments at the campus. Objects up to 100 metres are successfully detected by the radar system. The system demonstrates excellent ability to generate 2D SIMO SAR images, effectively identifying building and guard-box exteriors that are consistent with images from Google Earth. [ABSTRACT FROM AUTHOR]

Efficient and effective radar emitter recognition is critical for electronic support measurement (ESM) systems. However, in complex electromagnetic environments, intercepted pulse trains generally contain substantial data noise, including spurious and missing pulses. Currently, radar emitter recognition methods utilizing traditional artificial neural networks (ANNs) like CNNs and RNNs are susceptible to data noise and require intensive computations, posing challenges to meeting the performance demands of modern ESM systems. Spiking neural networks (SNNs) exhibit stronger representational capabilities compared to traditional ANNs due to the temporal dynamics of spiking neurons and richer information encoded in precise spike timing. Furthermore, SNNs achieve higher computational efficiency by performing event-driven sparse addition calculations. In this paper, a lightweight spiking neural network is proposed by combining direct coding, leaky integrate-and-fire (LIF) neurons, and surrogate gradients to recognize radar emitters. Additionally, an improved SNN for radar emitter recognition is proposed, leveraging the local timing structure of pulses to enhance adaptability to data noise. Simulation results demonstrate the superior performance of the proposed method over existing methods. [ABSTRACT FROM AUTHOR]

Under rainy conditions, atmospheric settling particles have significant depolarization effects on radar waves and thus affect the target detection performance. This paper establishes an extended target detection model for fully polarized radar under complex rainy conditions and proposes two polarization and waveform optimization methods by taking into account the transmission effect of rainfall. If the rainfall information is known, the polarization and waveform for transmitting and receiving are optimized by maximizing the system Signal-to-Clutter/Noise Ratio (SCNR). While the rainfall information is absent, the optimal transmitting polarization and waveform are obtained by directly compensating the transmission effect from non-rainfall optimization results using fully polarized radar observation parameters. Using measured data from the T-72 tank, The experimental results verify the effectiveness and robustness of the two methods in real scenarios and 4 dB on the improvement of SCNR can be achieved. [ABSTRACT FROM AUTHOR]

Beamforming is an effective way of resolving target direction and anti‐jamming in short wave (SW) radar systems. In conventional beamforming (CBF) at a certain frequency, to get high resolution, the array aperture should be increased, and this is often not allowed in practical applications. A new narrow beam forming (NBF) method for beam sharpening based on the generalised oblique projection (GOP) filter with a flexible parameter is proposed. This method uses a GOP filter bank to form deep nulls in the undesired azimuth range on the pattern and utilises the logic product process to synthesise the GOP filters' outputs and thus obtains a narrow beam. Compared to traditional beamforming methods, the result of NBF has the characteristics of narrower beam width and bigger side lobe suppression ratio (SLSR). Especially, a narrower beam can be obtained in the case of a small array aperture, which is valuable for practical applications. Experimental results of the range‐Doppler spectrum of short wave radar show that this narrow beam forming method can achieve super resolution of targets within a wide beam and greatly suppress clutter. Therefore, NBF can improve the azimuth resolution and achieve interference suppression in a conventional beam. [ABSTRACT FROM AUTHOR]

In the increasingly complex electromagnetic environment, main lobe jamming significantly degrades the performance of a wideband radar system. To mitigate this issue, this paper developed a wideband main lobe jamming suppression algorithm based on a distributed array radar. Firstly, this algorithm utilizes eigen-projection matrix processing in the main array to cancel out the main lobe jamming for main lobe maintenance, and then suppresses side lobe jamming through null constraint beamforming. Subsequently, the large aperture of the full array is leveraged to form a narrow beam directed toward the main lobe interference. Finally, joint beamforming using the minimum mean square error criterion is employed. In scenarios where both main lobe jamming and side lobe jamming exist, this algorithm can adaptively cancel main lobe wideband jamming, suppress side lobe wideband jamming, and effectively control the significant loss of the desired wideband signals caused by main lobe jamming within a smaller angular range. Simulation results validate the effectiveness of the algorithm. [ABSTRACT FROM AUTHOR]

A dual-polarized compact Vivaldi antenna with high gain performance is proposed for tree radar applications. The proposed design introduces an array configuration consisting of two pairs of two Vivaldi elements to optimize the operating bandwidth and gain while providing dual-polarization capability. To enhance the gain of the proposed antenna over a certain frequency range of interest, directors and edge slots are incorporated into each Vivaldi element. To further enhance the overall antenna gain, a metal back reflector is used. The measurement results of the fabricated antenna show that the proposed antenna achieves a high gain of 5.5 to 14.8 dBi over broadband from 0.5 GHz to 3 GHz. Moreover, it achieves cross-polarization discrimination larger than 20 dB, ensuring high polarization purity. The fabricated antenna is used to detect and image the defects inside tree trunks. The results show that the proposed antenna yields a better-migrated image with a clear defect region compared to that obtained by a commercial Horn antenna. [ABSTRACT FROM AUTHOR]

Nowadays, asteroid and comet exploration is one of the most important components of deep space exploration. Through asteroid and comet exploration missions, it is possible to reveal the history of the formation and evolution of the solar system, to understand the origin and evolution of the planets, and to improve scientific models and instruments. As a payload with the advantages of non-destructive, penetrating, and polarizing characteristics, ground-penetrating radar (GPR) has been widely used in lunar and Mars exploration, and will play an important role in planned asteroid and comet exploration missions. In this study, statistics on asteroid and comet exploration missions, scientific results, and space-based ground-penetrating radar (SB-GPR) utilization are presented for the three phases to date. According to the statistics, SB-GPR will play an important role in future Phase 2 and 3 missions. The focus of this study is on analyzing the mission flow, SB-GPR parameters, scientific objectives, and scientific results of the missions that have carried SB-GPR and those that are planned to carry SB-GPR, including the Hera, Rosetta, Castalia, and Tianwen-2 missions. On this basis, the development trends of asteroid and comet exploration missions, as well as the future development trends of SB-GPR design and signal interpretation, are discussed. [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]

Information on the spatial distribution of rainfall is required for many applications, including water and flood management. Weather radars can provide quantitative rainfall estimation over an area. Taking the example of an X-band radar installed at Mandhardev in the Western Ghats, we show that radar reflectivity data can have significant bias despite following standard calibration procedures. We report a technique to identify bias in the X-band radar reflectivity factor using collocated disdrometer observations and propose a methodology to correct it. Simultaneous data collected on 83 days during June–September of 2018 are used. Our results show that bias in the radar reflectivity factor reduced from –8.3 to –0.8 dB after correction. The estimated 83-day accumulated rainfall using uncorrected radar reflectivity data is 80% less compared to that of the disdrometer and the difference between the two reduces to 2% with correction. Bias in the estimated rainfall reduces from –30 to –0.7 mm day–1 and the RMSE reduces by 42%. There are days where daily rainfall from the two instruments agrees well with each other, while large differences exist on some days. We show that the sampling issue is one of the major sources of error, and its contribution depends on the nature/type of precipitating clouds, whereas uncertainties in the Z–R relationships account for about 15% difference. [ABSTRACT FROM AUTHOR]

Abstract: While software-defined radars can switch their transmitted waveform on the go, they cannot transmit all waveforms at the same time, meaning they must balance the advantages and drawbacks of each configuration. Here, we propose theoretically and demonstrate experimentally the universal radar, which can apply the desired waveform in the post-processing stage after the physical measurement has been performed. This method also allows using a single measurement of a scene to design and test any other radar in complex scenarios without having to take it to the field. The method is based on post-processing the frequency response measured by a synthetically broadband stepped-frequency continuous wave radar, such as a vector network analyzer. An algorithm for overcoming distortions due to moving targets is derived as well. This approach not only provides an ultra-wideband software-only defined radar, but it also enables the acquired data from any measured site to be used for the design and analysis of almost any other future radar system, significantly cutting the time and cost of new developments. The method suggests the creation of radar raw data repositories that can be shared across diversely different radar platforms. [ABSTRACT FROM AUTHOR]

Battlefield surveillance radar is usually 2-D radar, which cannot realize target three-dimensional localization, leading to poor resolution for the air target in the elevation dimension. Previous researchers have used the Traditional Height Finder Radar (HFR) or multiple 2-D radar networking to estimate the target three-dimensional location. However, all of them face the problems of high cost, poor real-time performance and high requirement of space–time registration. In this paper, Reconfigurable Intelligent Surfaces (RISs) with low cost are introduced into the 2-D radar to realize the target three-dimensional localization. Taking advantage of the wide beam of 2-D radar in the elevation dimension, several Unmanned Aerial Vehicles (UAVs) carrying RISs are set in the receiving beam to form multiple auxiliary measurement channels. In addition, the traditional 2-D radar measurements combined with the auxiliary channel measurements are used to realize the target three-dimensional localization by solving a nonlinear least square problem with a convex optimization method. For the proposed RIS-assisted target three-dimensional localization problem, the Cramer–Rao Lower Bound (CRLB) is derived to measure the target localization accuracy. Simulation results verify the effectiveness of the proposed 3-D localization method, and the influences of the number, the positions and the site errors of the RISs on the localization accuracy are covered. [ABSTRACT FROM AUTHOR]

This paper proposes a circularly-polarized (CP) metasurface antenna with ultra-wideband radar cross section (RCS) reduction. Initially, the relationship between RCS reduction and phase difference with different reflection amplitudes is analyzed theoretically. Inspired by the shared-aperture principle, a high-frequency resistive layer is introduced into the antenna element to further fulfill ultra-wideband RCS reduction and low-profile requirements. Numerical simulations demonstrate that the inserted layer has no effect on the radiation performance of the antenna array. Then, the proposed element and its orthogonal unit are arranged in a checkerboard pattern for good CP radiation. In this configuration, the proposed metasurface array antenna has a maximum gain of 16.7 dBi with 72.1% aperture efficiency. The bandwidth of 5 dB RCS reduction with x- and y-polarized incidences is 7.10–17.95 GHz and 7.34–17.85 GHz, respectively. The proposed design is verified by the fabrication of prototype antennas and measurements. [ABSTRACT FROM AUTHOR]

A high‐efficiency polarization conversion metasurface (PCM) is proposed and applied to radar cross section (RCS) reduction. The PCM can achieve cross‐polarization conversion with a polarization conversion ratio of over 97% from 13.9 to 30.3 GHz, covering K‐band. The low‐RCS antenna is designed from a circularly polarized patch antenna loading with the PCM. The antennas with and without PCM are fabricated and measured. The results show that the radiation performance is well preserved and the scattering performance is greatly improved. The average monostatic RCS reduction value reaches 14.6 dB in an ultra‐wideband from 12.2 to 30.2 GHz under normal incidence. What's more important, bistatic RCS is researched in detail and evident RCS reduction is achieved at different incidence angles. This research has potential applications in the field of stealth platform. [ABSTRACT FROM AUTHOR]

Noise impairs the performance of inverse synthetic aperture radar (ISAR) motion compensation, which induces severe defocusing under low signal‐to‐noise ratio environments. To overcome this issue, a novel similarity‐oriented (SO) method with a two‐domain denoising strategy is proposed. A PIxEl similarity‐oriented (PIE‐SO) denoising method designed for range‐Doppler (RD) domain and a modified RAnge Profile Similarity‐Oriented (RAP‐SO) denoising method designed for high‐resolution range profile (HRRP) matrix are included in the presented framework. Firstly, the PIE‐SO method directly performs a two‐dimensional fast Fourier transform on dechirp processed echo data to form a coarsely focusing ISAR image in the RD domain. Then the focusing image is separated from the noise background by virtue of pixel similarity, after which the noise is preliminarily removed. Subsequently, the coarsely denoised image is transformed into the HRRP matrix. Considering the range profile similarity impaired by noise is restored by the PIE‐SO denoising, a Laplacian regularised‐weighted nuclear norm proximal (LR‐WNNP) operator is proposed. The proposed modified RAP‐SO method, that is, the LR‐WNNP operator, exploits the low‐rank property of the HRRP matrix and the local similarity of adjacent HRRPs to reduce the residual noise. As a result, ISAR imaging quality is significantly improved. Comprehensive experiments illustrate the effectiveness and superiority of the presented method. [ABSTRACT FROM AUTHOR]

Radar systems are a topic of great interest, especially due to their extensive range of applications and ability to operate in all weather conditions. Modern radars have high requirements such as its resolution, accuracy and robustness, depending on the application. Noise Radar Technology (NRT) has the upper hand when compared to conventional radar technology in several characteristics. Its robustness to jamming, low Mutual Interference and low probability of intercept are good examples of these advantages. However, its signal processing is more complex than that associated to a conventional radar. Artificial Intelligence (AI)‐based signal processing is getting increasing attention from the research community. However, there is yet not much research on these methods for noise radar signal processing. The aim of the authors is to provide general information regarding the research performed on radar systems using AI and draw conclusions about the future of AI in noise radar. The authors introduce the use of AI‐based algorithms for NRT and provide results for its use. [ABSTRACT FROM AUTHOR]

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

Decision-level information fusion methods using radar and vision usually suffer from low target matching success rates and imprecise multi-target detection accuracy. Therefore, a robust target detection algorithm based on the fusion of frequency-modulated continuous wave (FMCW) radar and a monocular camera is proposed to address these issues in this paper. Firstly, a lane detection algorithm is used to process the image to obtain lane information. Then, two-dimensional fast Fourier transform (2D-FFT), constant false alarm rate (CFAR), and density-based spatial clustering of applications with noise (DBSCAN) are used to process the radar data. Furthermore, the YOLOv5 algorithm is used to process the image. In addition, the lane lines are utilized to filter out the interference targets from outside lanes. Finally, multi-sensor information fusion is performed for targets in the same lane. Experiments show that the balanced score of the proposed algorithm can reach 0.98, which indicates that it has low false and missed detections. Additionally, the balanced score is almost unchanged in different environments, proving that the algorithm is robust. [ABSTRACT FROM AUTHOR]

A doppler radar holds promising prospects in the field of indoor target localization. However, traditional Doppler radar systems suffer from high power consumption, large size, and noticeable radio frequency interference issues when transmitting carriers of different frequencies. Therefore, an ISABO-based (improved subtraction-average-based optimizer) target localization algorithm for a single-frequency Doppler radar is proposed in this paper. Firstly, a mathematical model for target localization is established according to the spatial geometric relationships during the target movement and the Doppler frequency-shift information in the single-frequency echo signal. Then, the optimization function is constructed with the target motion error as the optimization goal. Finally, an improved subtraction-average-based optimizer algorithm is proposed to solve the optimal parameters and realize the target positioning. The experimental results show that the proposed method can achieve centimeter-level localization accuracy with a cost-effective system structure. [ABSTRACT FROM AUTHOR]

High-velocity reentry objects suffer from plasma sheath during reentry through the atmosphere, which affects the propagation characteristics of radar signals. The existing research mainly focuses on the time-frequency characteristics of radar signals, neglecting the polarization within the geomagnetic environment. In this article, the distortion of polarization characteristics for L-band fully polarimetric radar is analyzed, and the influence of the geomagnetic field is evaluated. Based on the Appleton–Hartree formula, the refractive index of the plasma sheath considering the geomagnetic field is derived and analyzed. The error model for the polarization deflection (PD) of radar waves is then established based on the phase screen model. The magnetized plasma sheath causes the deflection of the polarization plane for the radar signal, leading to distortion in the polarization characteristics and the attenuation of the echo amplitude. Considering the typical parameters of the plasma sheath, the influences of the electron density, collision frequency, the geomagnetic field and the radar frequency are analyzed quantitatively. Specifically, the PD anomaly phenomenon is analyzed and the corresponding analytical result of radar frequency is also derived. The relationship between the geomagnetic field and the PD, as well as the attenuation, is considered to be approximately linear. The absorption attenuation is primarily influenced by collision frequency and is immune to the geomagnetic field. In addition, the increasing electron density expands them, whereas the radar frequency and the collision frequency have the opposite effect. Simulations with real SAR data from ALOS-2 demonstrate the distortions resulting from the magnetized plasma sheath on the radar echoes in an L-band fully polarimetric radar system. [ABSTRACT FROM AUTHOR]

To enhance the capability of identifying unknown emitters in open spaces, an open‐multiscale attention kernel (MSAK)‐convolutional neural network‐long short‐term memory (CNN‐LSTM) structure is proposed. To this end, first, a MSAK module and CNN‐LSTM structure are introduced, and then, the depth and complexity of the feature extraction network are improved to enhance its representation capability. To classify unknown emitters accurately, the MSAK‐CNN‐LSTM model is improved to obtain an open‐MSAK‐CNN‐LSTM model with open‐set recognition capability. Additionally, the two preprocessing procedures are summarised, and their strengths and weaknesses are compared. Experimental results show that the proposed open‐MSAK‐CNN‐LSTM model achieves satisfactory accuracy in identifying unknown emitters in open space. In addition, it has significant advantages in low signal‐to‐noise ratio (SNR) scenarios. [ABSTRACT FROM AUTHOR]

Near-range radar imaging (NRRI) has evolved into a vital technology with diverse applications spanning fields such as remote sensing, surveillance, medical imaging and non-destructive testing. The Pseudopolar Format Matrix (PFM) has emerged as a promising technique for representing radar data in a compact and efficient manner. In this paper, we present a comprehensive PFM description of near-range radar imaging. Furthermore, this paper also explores the integration of the Fractional Fourier Transform (FrFT) with PFM for enhanced radar signal analysis. The FrFT—a powerful mathematical tool for signal processing—offers unique capabilities in analysing signals with time-frequency localization properties. By combining FrFT with PFM, we have achieved significant advancements in radar imaging, particularly in dealing with complex clutter environments and improving target detection accuracy. Meanwhile, this paper highlights the imaging matrix form of FrFT under the PFM, emphasizing the potential for addressing challenges encountered in near-range radar imaging. Finally, numerical simulation and real-world scenario measurement imaging results verify optimized accuracy and computational efficiency with the fusion of PFM and FrFT techniques, paving the way for further innovations in near-range radar imaging applications. [ABSTRACT FROM AUTHOR]

This paper presents a novel, cost-effective technique for estimating the Doppler effect in the time domain using a single pulse and subsequently leveraging the precise Doppler value to recover the radar waveform. The proposed system offers several key advantages over existing techniques, including the ability to calculate the target speed without any frequency ambiguity and the ability to detect a wide range of target speeds. These two features are not available in any existing techniques, including the conventional moving target detection (MTD) processor. To ensure improved accuracy and robust estimation, the system employs ensemble and iterative techniques by recursively and efficiently reducing the Doppler residues from the signal. Furthermore, the proposed system demonstrates effective signal recovery of a well-known bi-phase code shape at low signal-to-noise ratios in just a few iterations. The performance evaluation of the new algorithm demonstrates its practicability and its superiority over traditional radar systems. Implementation on software-defined radio (SDR) reveals that the proposed system excels in Doppler estimation and signal recovery at low SNRs, demonstrating promising results. [ABSTRACT FROM AUTHOR]

Measuring heart rate is a critical component of assessing cardiac health and detecting potential heart diseases at an early stage. Various sensors, including electrocardiogram and photoplethysmograph, are commonly employed for this purpose. However, these conventional methods necessitate direct contact with the patient's skin which may be impractical or uncomfortable in situations involving patients with skin diseases or burn injuries. To address these limitations, a concerted effort has been to develop non‐contact methods leveraging frequency‐modulated continuous‐wave multiple‐input multiple‐output radar systems. Recent studies have illustrated the sensitivity of the phase component of received signals to micro‐motion, presenting a promising avenue for effective heart rate estimation. However, existing literature predominantly focuses on single‐antenna setups, overlooking the potential benefits offered by multiple‐input multiple‐output systems, which provide diverse channels with varying precision levels. This correspondence introduces a novel phase extraction model grounded in the argument of the analytic signal derived from both in‐phase and quadrature channels. Furthermore, leveraging the normal equation, we establish the feasibility of optimizing weights assigned to individual virtual antennas to achieve a robust approximation of ground truth data. [ABSTRACT FROM AUTHOR]

A large body of evidence suggests that low parasite carriage in Plasmodium falciparum asymptomatic infection is required for the maintenance of malaria immunity. However, the fact that treating such infections has little to no impact on subsequent clinical malaria is rarely noted. In this paper, we review data and argue that low-density parasite carriage in asymptomatic infection may not support host immune processes and that parasites are virtually under the host's immunological radar. We also discuss factors that may be constraining parasitemia in asymptomatic infections from reaching the threshold required to cause clinical symptoms. A thorough understanding of this infectious reservoir is essential for malaria control and eradication because asymptomatic infections contribute significantly to Plasmodium transmission. [ABSTRACT FROM AUTHOR]

Extensive studies have been carried out on the characteristics of quantum radar cross section (QRCS) of targets. However, one crucial question related to multi-photon quantum radar cross section (M-QRCS) for targets in the atmospheric medium has not been explored yet. Understanding this question is vital for target detection and identification of quantum radar. This paper presents a universal method to solve M-QRCS in a homogeneous atmospheric medium (HAM-QRCS). The process is based on the photon wave function in a homogeneous atmospheric medium and the interaction mechanism of multi-photon and multiple atoms. It is suitable for analyzing the HAM-QRCS characteristics of targets of arbitrary shapes. The simulation results show that the molecules, particles, and other factors in the atmospheric medium cause the signal photons' energy to decrease and the propagation direction to change, leading to a decrease in the target return responses. However, in a specific angle range, as the photon number increases, the main lobe and first side lobe structures of the bistatic HAM-QRCS response are enhanced. These findings can be utilized to design target detection strategies and optimize stealth target structures of the quantum radar in the atmospheric medium. [ABSTRACT FROM AUTHOR]

The X-band phased array radar offers faster scanning speed and higher spatial resolution compared to the S-band radar, making it capable of enhancing tornado monitoring and early warning capabilities. This study analyzed the characteristics and nowcasting signals of a tornado case that occurred on June 16, 2022 in the Guangzhou region. Our findings indicate that the violent contraction of rotation radius and the dramatic increase in rotation speed were important signal characteristics associated with tornado formation. The X-band phased array radar, with its high temporal and spatial resolution, provided an opportunity to capture early warning signals from polarimetric characteristics. The X-band phased array radar demonstrated noteworthy ability to identify apparent tornado vortex signature (TVS) features in a 10-minute lead time, surpassing the capabilities of the CINRAD/SA radar. Additionally, due to its higher scanning frequency, the Xband phased-array radar was capable of consistently identifying TVS with shorter intervals, enabling a more precise tracking of the tornado's path. The application of professional radars, in this case, provides valuable insights for the monitoring of evolutions of severe local storms and even tornadoes and the issuance of early warning signals. [ABSTRACT FROM AUTHOR]

This paper presents three refinements in ground-based radar interferometer (GB-radar) measurement for bridge load testing: (1) GB-radar phase jumps were detected for the first time on bridge tower displacement monitoring, and a recovery method is presented to obtain the correct unwrapped value; (2) a precise displacement projection method considering target deformation was exploited, and a case study of the Fifth Nanjing Yangtze River Bridge (FNYRB) GB-radar campaign shows that a centimeter-level compensation can be achieved; (3) a post-construction settlement phenomenon was found during the FNYRB static load tests, characterized by 0.31 mm/min, which accumulated up to 25 mm. In addition, the dynamic monitoring capabilities of GB-radar for the bridge tower and girder were verified, highlighting its potential for bridge structural health monitoring (SHM). The insights gained from this study offer valuable recommendations for future GB-radar bridge displacement monitoring. [ABSTRACT FROM AUTHOR]

Autonomous driving technology is considered the trend of future transportation. Millimeter-wave radar, with its ability for long-distance detection and all-weather operation, is a key sensor for autonomous driving. The development of various technologies in autonomous driving relies on extensive simulation testing, wherein simulating the output of real radar through radar models plays a crucial role. Currently, there are numerous distinctive radar modeling methods. To facilitate the better application and development of radar modeling methods, this study first analyzes the mechanism of radar detection and the interference factors it faces, to clarify the content of modeling and the key factors influencing modeling quality. Then, based on the actual application requirements, key indicators for measuring radar model performance are proposed. Furthermore, a comprehensive introduction is provided to various radar modeling techniques, along with the principles and relevant research progress. The advantages and disadvantages of these modeling methods are evaluated to determine their characteristics. Lastly, considering the development trends of autonomous driving technology, the future direction of radar modeling techniques is analyzed. Through the above content, this paper provides useful references and assistance for the development and application of radar modeling methods. [ABSTRACT FROM AUTHOR]

The integration of both sensing and communication functions is a crucial feature for future communication systems. This paper considers a novel scenario where a radar covers multiple small-cell base stations which operate in different spectra. Due to the limited spectra resource, the radar needs to reuse the spectra of one BS for tracking. To suppress interference and improve performance, we propose a framework including two stages and the corresponding communication and sensing algorithms. Specifically, at the detection stage, we aim to minimize the system transmit power while maintaining the performance of both radar and communication. At the tracking stage, our goal is to maximize the radar sensing performance under a given power budget, while ensuring communication quality. Due to the complexity of the original problem, we address it by introducing auxiliary variables and proposing a penalty dual decomposition-based algorithm, enabling us to solve a more tractable form of the problem. In the inner loop, we propose a concave–convex procedure-based algorithm to handle the optimization problem, while adopting the block coordinate descent algorithm to iteratively update the variables. In the outer loop, we update the penalty term or Lagrange multipliers. Furthermore, we propose a radar target parameter estimation algorithm based on successive interference cancellation, which aims to mitigate communication interference. Finally, numerical simulations demonstrate the effectiveness of the proposed system, and the superior performance of our proposed algorithms over benchmark algorithms. [ABSTRACT FROM AUTHOR]

A THz radar, with its wide bandwidth, is capable of high‐resolution imaging down to the centimeter scale. In this study, a THz radar is applied to detect hydrometeors generated in a spray chamber. The observed backscattering signals show fluctuations at centimeter scales, indicating various hydrometeor distribution patterns along the radar beam. A co‐located High‐Speed Imaging (HSI) sensor is used to measure the Drop Size Distributions (DSD) in the spray chamber. The radar sampling beam is well aligned with the HSI probes, allowing an objective comparison between the remote sensing and in situ observations. In this study, the observed radar power is compared with the power estimated from the HSI measurements. Results show great consistency, with power difference smaller than 0.5 dB. This study demonstrates the feasibility and great potential of using a THz radar for ultra‐high‐resolution observations of clouds in a laboratory facility, and in the real atmosphere. Plain Language Summary: An improved understanding of how precipitation forms in clouds calls for new observational instruments and novel measurement strategies. Radars operating at ultra‐high frequency (a.k.a. THz radars) are known for their unprecedented resolution down to centimeter scale. Here, for the first time, we applied a THz radar to detect hydrometeors generated in a spray chamber. An additional in situ probe is utilized within the chamber to validate the THz radar observations. The radar beam is directed through the same volume that is sampled by the in situ probe, thus enabling an objective comparison between the in situ and remote sensing measurements. Different hydrometeor distributions are generated in the chamber, each being jointly observed by the radar and the in situ probes. Results show that the THz radar measurements agree well with the radar power calculated from the in situ observations. This unique experiment indicates promising scenarios of applying THz radar to cloud and precipitation studies in a laboratory facility and in the real atmosphere. Key Points: A novel experiment is conducted by applying a THz radar to detect hydrometeors generated in a spray chamberGood agreement between in situ and radar observations demonstrates the unique capability of THz radar for cloud and precipitation studiesImplications of applying THz radar to design novel observational strategies for solving fundamental gaps in cloud physics are demonstrated [ABSTRACT FROM AUTHOR]

Seabirds interact with fishing vessels to consume fishing discards and baits, sometimes resulting in incidental capture (bycatch) and the death of the bird, which has clear conservation implications. To understand seabird–fishery interactions at large spatiotemporal scales, researchers are increasing their use of simultaneous seabird and fishing vessel tracking. However, vessel tracking data can contain gaps due to technical problems, illicit manipulation, or lack of adoption of tracking monitoring systems. These gaps might lead to underestimating the fishing effort and bycatch rates and jeopardize the effectiveness of marine conservation. We deployed bird‐borne radar detector tags capable of recording radar signals from vessels. We placed tags on 88 shearwaters (Calonectris diomedea, Calonectris borealis, and Calonectris edwardsii) that forage in the northwestern Mediterranean Sea and the Canary Current Large Marine Ecosystem. We modeled vessel radar detections registered by the tags in relation to gridded automatic identification system (AIS) vessel tracking data to examine the spatiotemporal dynamics of seabird–vessel interactions and identify unreported fishing activity areas. Our models showed a moderate fit (area under the curve >0.7) to vessel tracking data, indicating a strong association of shearwaters to fishing vessels in major fishing grounds. Although in high‐marine‐traffic regions, radar detections were also driven by nonfishing vessels. The tags registered the presence of potential unregulated and unreported fishing vessels in West African waters, where merchant shipping is unusual but fishing activity is intense. Overall, bird‐borne radar detectors showed areas and periods when the association of seabirds with legal and illegal fishing vessels was high. Bird‐borne radar detectors could improve the focus of conservation efforts. [ABSTRACT FROM AUTHOR]

With the continuous development of automotive intelligence, vehicle occupant detection technology has received increasing attention. Despite various types of research in this field, a simple, reliable, and highly private detection method is lacking. This paper proposes a method for vehicle occupant detection using millimeter-wave radar. Specifically, the paper outlines the system design for vehicle occupant detection using millimeter-wave radar. By collecting the raw signals of FMCW radar and applying Range-FFT and DoA estimation algorithms, a range–azimuth heatmap was generated, visually depicting the current status of people inside the vehicle. Furthermore, utilizing the collected range–azimuth heatmap of passengers, this paper integrates the Faster R-CNN deep learning networks with radar signal processing to identify passenger information. Finally, to test the performance of the detection method proposed in this article, an experimental verification was conducted in a car and the results were compared with those of traditional machine learning algorithms. The findings indicated that the method employed in this experiment achieves higher accuracy, reaching approximately 99%. [ABSTRACT FROM AUTHOR]

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

The authors deal with the problem of the design and manufacture of a high‐resolution FMCW radar for drone detection and classification. The difficulties of the problem are discrete clutter reduction and spurious phase noise mitigation. The discrete clutter is due to the reflected signals from land vehicles, birds etc., while the spurious phase noise is inherent in the radar signal due to the phase‐locked loop component and leakage between the transmitting and receiving paths. Both spurious phase noise and clutter will increase the system noise level and hence reduce the probability of detection of small targets such as drones and induce false alarms on the radar screen. In order to reduce discrete clutter, the authors propose a method to separate a drone from discrete clutter based on the design of the radar system parameters for a drone and its propeller detection, target's Doppler dispersion and moving characteristics. For spur mitigation, a method that focuses on the design of the isolation coefficient between transmitting and receiving paths to decrease the power of spurs below the minimum power requirement at the input of the analogue‐to‐digital converter is introduced. The results were applied by the authors to the development and manufacture of a radar with the given specifications for drone detection and classification. Different laboratory and field tests show that the spurs are mitigated and the drones are separated from discrete clutter with a range and accuracy better than the one recently published. [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]

Detection of space objects is a key component of space situational awareness, which could help prevent and minimise space collisions. While there have been lots of radar systems designed to detect space objects, few of them have dealt with long baseline distributed bistatic pairs. The authors focus on the feasibility of long baseline bistatic radars, which can be extended for the multistatic case; and the performance of the multistatic system for a target at different altitudes assuming one transmitter over three different scenarios: a cluster of receivers, receivers spread throughout the world and the combination of the two previous cases. To analyse the performance the multiple‐input‐multiple‐output (MIMO) ambiguity function (AF) will be employed. The results of the MIMO AF show how the fusion of different bistatic pairs improves the detection capabilities. Moreover, when the different radar measurements are coherently summed in the MIMO AF, the uncertainty on the location of the target is reduced. [ABSTRACT FROM AUTHOR]

Long baseline bistatic radar systems herald enhanced sensitivity and metric accuracy for space objects in geosynchronous orbits and beyond. Radio telescopes are ideal participants in such a system; in particular, they often feature large apertures with low‐noise temperatures and have stable, synchronised clocks. Pairing radio telescopes with high‐power radars creates new methodologies for Space Domain Awareness. This paper describes long baseline bistatic measurements using the Millstone Hill Radar in the USA, the Tracking and Imaging Radar in Germany, multiple receivers of the enhanced multi‐element remotely linked interferometer network array in the United Kingdom, and the Westerbork Synthesis Radio Telescope in the Netherlands. The authors, a Research Task Group formed by the NATO Science and Technology Organisation Sensors and Electronic Technology Panel (SET‐293), performed novel bistatic and monostatic radar imaging experiments with real on‐orbit tumbling rocket bodies. These experiments on tumbling objects at near‐geosynchronous orbits highlight successful demonstrations of advanced bistatic Doppler characterisation across diverse imaging geometries. Specialised Doppler processing on tumbling targets, such as the Doppler superpulse algorithm, enables high‐fidelity rotation period estimation and determination of minimum target size. [ABSTRACT FROM AUTHOR]

P-band radar interferometry is a promising tool for digital terrain modelling in forested areas such as the Amazon due to the ability of P-band to penetrate densely forested areas. The quality of Digital Terrain Models (DTMs) is a primary requirement for many geoscience applications because DTM accuracy is an essential variable for characterizing landform shapes. This article presents a multicriteria evaluation of the overall quality of a digital terrain model produced using P-band SAR interferometric processing. The evaluation considers behaviours related to elevation and slope and analyses the impact of landscape properties such as slope and aspect. The multicriteria accuracy of the study showed promising results about the realism of landforms, according to applied geomorphological criteria, and also in the visual comparison between the produced DTM and the reference. The study provided an estimate of the accuracy of P-band radar DTM in terms of elevation with a standard deviation of 2.36 m and in terms of slope with a mean error of approximately −4°, a standard deviation of 4.74°, and a RMSE (Root Mean Square Error) slope error of 6.14°, using a reference DTM derived from an airborne LiDAR (Light Detection and Ranging) survey. The spatial behaviour of these errors and their sensitivity to slope and aspect were analysed. The investigation of hydrographic inconsistencies in the DTM was conducted based on various criteria, including identifying depressions along watercourses and adhering to Horton's law. Finally, the effects of the acquisition, processing, and resampling processes are revealed through indicators such as the rose histogram. This multicriteria study demonstrates the suitability of airborne P-band radar interferometry for digital elevation modelling in tropical rainforest environments. [ABSTRACT FROM AUTHOR]

Aiming at the requirement of radar wave absorption in automotive radar tests for both the 24 GHz and 77 GHz vehicle radar bands, two ultra-thin dual-band circuit analog absorber designs based on frequency-selective conductive film have been proposed. They are obtained by first optimising the equivalent circuit parameters to achieve the desired reflection response, then determining the physical parameters (geometrical size and square resistance) of unit cells from the equivalent circuit parameters, and finally fine-tuning these physical parameters. Both designs employ unit cell based on the nested structure of conductive square ring and a conductive square patch, which has the advantages of simple structure, ease of processing, high design freedom, and good polarisation and angular stability. The 1st design employs different square resistance values for the conductive ring and patch, while the 2nd design employs a single square resistance value for both conductive shapes. Simulation results show that, for each of the two designs, the lower and upper 70% absorption or −10 dB reflection bands simultaneously cover the 24 GHz and 77 GHz vehicle radar bands, respectively, with good angular stability. The dual-band coverage keeps stable until the elevation incident angle reaches 20° and 50° for the 1st and 2nd designs, respectively. A realistic sample is processed regarding the 2nd design and its dual-band absorption performance has been verified by experimental results, which have good agreement with the simulated counterparts. [ABSTRACT FROM AUTHOR]

Based on the characteristic of ubiquitous radar emitting low-gain wide beam, a method of long-time coherent integration (LTCI) is required to enhance target detection capability. However, high-speed and highly maneuverable targets can cause Doppler frequency migration (DFM), range migration (RM), and velocity ambiguity (VA), severely degrading the performance of LTCI. Additionally, the number of targets is unknown and variable, and the presence of clutter further complicates the target tracking problem. To address these challenges, we propose a hybrid integration method to achieve joint detection and estimation of multiple high-speed, and highly maneuverable targets. Firstly, we compensate for first-order RM using the keystone transform (KT) and generate corresponding sub-range-Doppler (SRD) planes with different folding factors to achieve VA compensation. These SRD planes are then stitched together to form an extended range-Doppler (ERD) plane, which covers a broader velocity range. Secondly, during the track-before-detect (TBD) process, tracking is performed directly on the ERD plane. We use the sequential Monte Carlo (SMC) approximation of the probability hypothesis density (PHD) to propagate multi-target states. Additionally, we propose an amplitude-based adaptive prior distribution method and a line spread model (LSM) observation model to compensate for DFM. Since the acceleration of the target is included in the particle state, using particles to search for DFM does not increase the computational load. To address the issue of misclassifying mirror targets as real targets in the SRD plane, we propose a particle space projection method. By stacking the SRD planes to create a folding range-Doppler (FRD) space, particles are projected along the folding factor dimension, and then, the particles are clustered to eliminate the influence of the mirror targets. Finally, through simulation experiments, the superiority of the LSM for targets with acceleration was demonstrated. In comparative experiments, the proposed method showed superior performance and robustness compared to traditional methods, achieving a balance between performance and computational efficiency. Furthermore, the proposed method's capability to detect and track multiple high-speed and highly maneuverable targets was validated using actual data from a ubiquitous radar system. [ABSTRACT FROM AUTHOR]

Synthetic-aperture radar (SAR) can work in all weather conditions and at all times, and satellite-borne radar has the characteristics of short revisiting period and large imaging width. Therefore, satellite-borne synthetic-aperture radar has been widely deployed, and the SAR images have been widely used in geographic mapping, radar interpretation, ship detection, and other fields. Satellite-borne synthetic-aperture radar is also susceptible to various types of intentional or unintentional interference during the imaging process, and because the interference is a direct wave, its power is much stronger than the wave reflected by targets. As a common interference pattern, radio-frequency interference widely exists in various satellite-borne synthetic-aperture radars, which seriously deteriorates SAR image quality. In order to solve the above problems, this paper proposes a feature decomposition network to suppress interference based on regularization optimization. The contributions of this work are as follows: 1. By analyzing the performance limitations of the existing methods, this work proposes a novel regularization method for radio-frequency interference suppression tasks. From the perspective of data distribution histograms and residual components, the proposed method eliminates the variable components introduced by common regularization, greatly reduces the difficulty of data mapping, and significantly improves its robustness and performance. 2. This work proposes a feature decomposition network, where the feature decomposition module contains two parts; one part only represents the interference signal, and the other part only represents the radar signal. The neurons representing the interference signal are discarded, and the neurons representing the radar signal are used as input for the subsequent network. A cosine similarity constraint is used to separate the interference from the network as much as possible. Finally, this method is validated on the MiniSAR dataset and Sentinel-1A dataset. [ABSTRACT FROM AUTHOR]