Accurate calibration of China's new generation ground-based polarimetric radar(GR) network is crucial yet challenging. Although application of the Dual-frequency Precipitation Radar(DPR) of the Global Precipitatio...Accurate calibration of China's new generation ground-based polarimetric radar(GR) network is crucial yet challenging. Although application of the Dual-frequency Precipitation Radar(DPR) of the Global Precipitation Measurement Core Observatory for GR assessment is well-established, current methodologies are inherently limited. Focusing on three GRs in the Guangdong-Hong Kong-Macao Greater Bay Area(GBA)—strategically selected for their high overlapping coverage(>65%) and distinct from single GR or less dense coverage studies—this work introduces key refinements by integrating innovative enhancements into the volume-matching method(VMM), reflecting a systematic approach to mitigating potential error sources. Specifically, we integrate: 1) a novel frequency correction method that adapts to DPR-observed precipitation phase and type, replacing assumption-based polynomial fitting;and 2) a precise beam time-difference matching approach(accuracy < 1 s) to minimize temporal mismatch errors, which improves upon coarser time averaging methods. Furthermore, we developed statistically robust, optimized threshold criteria based on systematic sensitivity analyses using 11 quality control factors, including precipitation type, bright band effects, and attenuation correction limitations. These criteria establish an enhanced protocol designed to minimize errors arising from instrumental, frequency, and scanning differences. Application of this enhanced methodology to the GBA GRs(2021–2023) yielded a significantly improved matching accuracy(correlation coefficient, CC: 0.90–0.95;standard deviation,STD: 1.2–1.6 dB). A unique contribution of this work is the quantitative estimation of historical calibration errors and operational stability, which was achieved by linking VMM results with operational GR calibration and maintenance records. This analysis revealed decreasing STD trends and identified specific calibration-related events, such as an underestimation of approximately 2.43 dB for the Shenzhen radar following calibration in 2023. Consequently, the refined methodology provides a robust framework for ongoing GR network monitoring and offers a validated pathway for authenticating China's Fengyun-3G(FY-3G) satellite precipitation measurement radar(PMR) data.展开更多
In situ density and moisture content of asphalt pavement are essential controlling parameters that require accurate measurement for quality control and quality assurance purposes.The ground-penetrating radar(GPR)techn...In situ density and moisture content of asphalt pavement are essential controlling parameters that require accurate measurement for quality control and quality assurance purposes.The ground-penetrating radar(GPR)technique could provide non-destructive,non-contact,and full-coverage estimations of pavement density and moisture content.However,the technical readiness and drawbacks,including prediction models,signal processing algorithms,and testing hardware,remain unclear for agencies and construction practitioners,impeding large-scale implementations.This paper aims to provide a thorough review of the theoretical background and current practices of using GPR for non-destructive measurements of asphalt pavement density and moisture content during construction,thereby allowing for real-time correction of over-or under-compaction on site.The principles and applications of GPR-based density and moisture content prediction models were comprehensively summarized.Their strengths and limitations were discussed.Cutting-edge GPR equipment suitable for such applications was introduced,including their system components,application scenarios,and inherent limitations.Factors affecting prediction accuracy were analyzed.Advanced signal processing algorithms were discussed in the end,along with the in-place calibration procedure for aggregate dielectric constants.The reviewed technique could be a guiding tool for real-time monitoring of asphalt pavement density and moisture content using GPR,offering practical insights for future development and standardized deployment in construction quality management.展开更多
This paper addresses weak target detection problem for bistatic radar via a pseudo-spectrum(PS)based track-before-detect(TBD).Generally,PS-TBD estimates target position and velocity by means of pseudo-spectrum constru...This paper addresses weak target detection problem for bistatic radar via a pseudo-spectrum(PS)based track-before-detect(TBD).Generally,PS-TBD estimates target position and velocity by means of pseudo-spectrum construction in the discrete measurement space and accurate energy accumulation in mixed coordinates.However,the grids within the polar sensing region of the receivers in the bistatic radar are not aligned.Traditional PS-TBD can not directly process these measurements.In this paper,a PS-TBD method for bistatic radar is proposed to overcome this problem.Each cell in the measurement space of the receivers is mapped to the aligned Cartesian coordinates and predicted to the integration frame according to the assumed filter velocity.A PS is formulated centered on the predicted Cartesian position.Then the samples of the pseudo-spectra are accumulated to the nearest cell around the predicted Cartesian position.The procedure of the energy integration is derived in detail.Simulation results validate the efficacy of the proposed method in terms of detection accuracy and parameter estimation.展开更多
In high-intensity electromagnetic warfare,radar systems are persistently subjected to multi-jammer attacks,including potentially novel unknown jamming types that may emerge exclusively under wartime conditions.These j...In high-intensity electromagnetic warfare,radar systems are persistently subjected to multi-jammer attacks,including potentially novel unknown jamming types that may emerge exclusively under wartime conditions.These jamming signals severely degrade radar detection performance.Precise recognition of these unknown and compound jamming signals is critical to enhancing the anti-jamming capabilities and overall reliability of radar systems.To address this challenge,this article proposes a novel open-set compound jamming cognition(OSCJC)method.The proposed method employs a detection-classification dual-network architecture,which not only overcomes the false alarm and misdetection issues of traditional closed-set recognition methods when dealing with unknown jamming but also effectively addresses the performance bottleneck of existing open-set recognition techniques focusing on single jamming scenarios in compound jamming environments.To achieve unknown jamming detection,we first employ a consistency labeling strategy to train the detection network using diverse known jamming samples.This strategy enables the network to acquire highly generalizable jamming features,thereby accurately localizing candidate regions for individual jamming components within compound jamming.Subsequently,we introduce contrastive learning to optimize the classification network,significantly enhancing both intra-class clustering and inter-class separability in the jamming feature space.This method not only improves the recognition accuracy of the classification network for known jamming types but also enhances its sensitivity to unknown jamming types.Simulations and experimental data are used to verify the effectiveness of the proposed OSCJC method.Compared with the state-of-the-art open-set recognition methods,the proposed method demonstrates superior recognition accuracy and enhanced environmental adaptability.展开更多
In this paper,the joint design of transmit and receive beamformers for transmit subaperturing multiple-input-multiple-output(TS-MIMO)radar is investigated,aiming to enhance its low probability of intercept(LPI)capabil...In this paper,the joint design of transmit and receive beamformers for transmit subaperturing multiple-input-multiple-output(TS-MIMO)radar is investigated,aiming to enhance its low probability of intercept(LPI)capability.The main objective is to simultaneously minimize the transmission power,suppress the transmit sidelobe levels,and minimize the probability of intercept,thus bolstering the LPI performance of the radar system while maintaining the desired target detection performance.An alternative optimization method is proposed to jointly optimize the transmit and receive beamformers,yielding an unified LPI optimization framework.Particularly,the proposed iterative algorithm based on the Lagrange duality theory for transmit beamforming is more efficient than the conventional convex optimization method.Numerical experiments highlight the effectiveness of the proposed approach in sidelobe suppression and computational efficiency.展开更多
Motor imbalance is a critical failure mode in rotating machinery,potentially causing severe equipment damage if undetected.Traditional vibration-based diagnostic methods rely on direct sensor contact,leading to instal...Motor imbalance is a critical failure mode in rotating machinery,potentially causing severe equipment damage if undetected.Traditional vibration-based diagnostic methods rely on direct sensor contact,leading to installation challenges and measurement artifacts that can compromise accuracy.This study presents a novel radar-based framework for non-contact motor imbalance detection using 24 GHz continuous-wave radar.A dataset of 1802 experimental trials was sourced,covering four imbalance levels(0,10,20,30 g)across varying motor speeds(500–1500 rpm)and load torques(0–3 Nm).Dual-channel in-phase and quadrature radar signals were captured at 10,000 samples per second for 30-s intervals,preserving both amplitude and phase information for analysis.A multi-domain feature extraction methodology captured imbalance signatures in time,frequency,and complex signal domains.From 65 initial features,statistical analysis using Kruskal–Wallis tests identified significant descriptors,and recursive feature elimination with Random Forest reduced the feature set to 20 dimensions,achieving 69%dimensionality reduction without loss of performance.Six machine learning algorithms,Random Forest,Extra Trees Classifier,Extreme Gradient Boosting,Categorical Boosting,Support Vector Machine with radial basis function kernel,and k-Nearest Neighbors were evaluated with grid-search hyperparameter optimization and five-fold cross-validation.The Extra Trees Classifier achieved the best performance with 98.52%test accuracy,98%cross-validation accuracy,and minimal variance,maintaining per-class precision and recall above 97%.Its superior performance is attributed to its randomized split selection and full bootstrapping strategy,which reduce variance and overfitting while effectively capturing the nonlinear feature interactions and non-normal distributions present in the dataset.The model’s average inference time of 70 ms enables near real-time deployment.Comparative analysis demonstrates that the radar-based framework matches or exceeds traditional contact-based methods while eliminating their inherent limitations,providing a robust,scalable,and noninvasive solution for industrial motor condition monitoring,particularly in hazardous or space-constrained environments.展开更多
Within the domain of Intelligent Group Systems(IGSs),this paper develops a resourceaware multitarget Constant False Alarm Rate(CFAR)detection framework for multisite MIMO radar systems.It underscores the necessity of ...Within the domain of Intelligent Group Systems(IGSs),this paper develops a resourceaware multitarget Constant False Alarm Rate(CFAR)detection framework for multisite MIMO radar systems.It underscores the necessity of managing finite transmit and receive antennas and transmit power systematically to enhance detection performance.To tackle the multidimensional resource optimization challenge,we introduce a Cooperative Transmit-Receive Antenna Selection and Power Allocation(CTRSPA)strategy.It employs a perception-action cycle that incorporates uncertain external support information to optimize worst-case detection performance with multiple targets.First,we derive a closed-form expression that incorporates uncertainty for the noncoherent integration squared-law detection probability using the Neyman-Pearson criterion.Subsequently,a joint optimization model for antenna selection and power allocation in CFAR detection is formulated,incorporating practical radar resource constraints.Mathematically,this represents an NPhard problem involving coupled continuous and Boolean variables.We propose a three-stage method—Reformulation,Node Picker,and Convex Power Allocation—that capitalizes on the independent convexity of the optimization model for each variable,ensuring a near-optimal result.Simulations confirm the approach's effectiveness,efficiency,and timeliness,particularly for large-scale radar networks,and reveal the impact of threat levels,system layout,and detection parameters on resource allocation.展开更多
The Global Precipitation Measurement(GPM)dual-frequency precipitation radar(DPR)products(Version 07A)are employed for a rigorous comparative analysis with ground-based operational weather radar(GR)networks.The reflect...The Global Precipitation Measurement(GPM)dual-frequency precipitation radar(DPR)products(Version 07A)are employed for a rigorous comparative analysis with ground-based operational weather radar(GR)networks.The reflectivity observed by GPM Ku PR is compared quantitatively against GR networks from CINRAD of China and NEXRAD of the United States,and the volume matching method is used for spatial matching.Additionally,a novel frequency correction method for all phases as well as precipitation types is used to correct the GPM Ku PR radar frequency to the GR frequency.A total of 20 GRs(including 10 from CINRAD and 10 from NEXRAD)are included in this comparative analysis.The results indicate that,compared with CINRAD matched data,NEXRAD exhibits larger biases in reflectivity when compared with the frequency-corrected Ku PR.The root-mean-square difference for CINRAD is calculated at 2.38 d B,whereas for NEXRAD it is 3.23 d B.The mean bias of CINRAD matched data is-0.16 d B,while the mean bias of NEXRAD is-2.10 d B.The mean standard deviation of bias for CINRAD is 2.15 d B,while for NEXRAD it is 2.29 d B.This study effectively assesses weather radar data in both the United States and China,which is crucial for improving the overall consistency of global precipitation estimates.展开更多
Synthetic aperture radar(SAR)aboard SEASAT was first launched in 1978.At the beginning of the 21st century,the Chinese remote sensing community recognized the urgent need to develop domestic SAR capabilities.Unlike sc...Synthetic aperture radar(SAR)aboard SEASAT was first launched in 1978.At the beginning of the 21st century,the Chinese remote sensing community recognized the urgent need to develop domestic SAR capabilities.Unlike scatterometers and al-timeters,space-borne SAR offers high-resolution images of the ocean,regardless of weather conditions or time of day.SAR imagery provides rich information about the sea surface,capturing complicated dynamic processes in the upper layers of the ocean,particular-ly in relation to tropical cyclones.Over the past four decades,the advantages of SAR have been increasingly recognized,leading to notable marine applications,especially in the development of algorithms for retrieving wind and wave data from SAR images.This study reviews the history,progress,and future outlook of SAR-based monitoring of sea surface wind and waves.In particular,the ap-plicability of various SAR wind and wave algorithms is systematically investigated,with a particular focus on their performance un-der extreme sea conditions.展开更多
A microwave photonic prototype for concurrent radar detection and spectrum sensing is proposed.A direct digital synthesizer and an analog electronic circuit are integrated to generate an intermediate frequency(IF)line...A microwave photonic prototype for concurrent radar detection and spectrum sensing is proposed.A direct digital synthesizer and an analog electronic circuit are integrated to generate an intermediate frequency(IF)linearly frequency-modulated(LFM)signal ranging from 2.5 to 9.5 GHz,with an instantaneous bandwidth of 1 GHz.The IF LFM signal is converted to the optical domain via an intensity modulator and filtered by a fiber Bragg grating to generate two second-order sidebands.The two sidebands beat each other to generate a frequency-and-bandwidth-quadrupled LFM signal.By changing the center frequency of the IF LFM signal,the radar function can be operated within 8 to 40 GHz.One second-order sideband works in conjunction with the stimulated Brillouin scattering gain spectrum for microwave frequency measurement,providing an instantaneous measurement bandwidth of 2 GHz and a frequency measurement range from 0 to 40 GHz.The prototype is demonstrated to be capable of achieving a range resolution of 3.75 cm,a range error of less than ±2 cm,a radial velocity error within ±1 cm∕s,delivering clear imaging of multiple small targets,and maintaining a frequency measurement error of less than ±7 MHz and a frequency resolution of better than 20 MHz.展开更多
Radar cross section(RCS)plays a critical role in modeling target scattering characteristics and enhancing the precision of target detection and localization in integrated sensing and communication(ISAC)systems.This pa...Radar cross section(RCS)plays a critical role in modeling target scattering characteristics and enhancing the precision of target detection and localization in integrated sensing and communication(ISAC)systems.This paper investigates the human body RCS at 26 GHz via multiangle channel measurements under different clothing conditions.Based on calibrated electromagnetic(EM)parameters,the RCS characteristics of the human body in far-field conditions are analyzed using ray-tracing(RT)simulations.Some suggestions for the design of ISAC systems are also discussed.The results provide a solid theoretical foundation and practical reference for the modeling of target scattering characteristics for ISAC channels.展开更多
The meteor radar can detect the zenith angle,azimuth,radial velocity,and altitude of meteor trails so that one can invert the wind profiles in the mesosphere and low thermosphere(MLT)region,based on the Interferometri...The meteor radar can detect the zenith angle,azimuth,radial velocity,and altitude of meteor trails so that one can invert the wind profiles in the mesosphere and low thermosphere(MLT)region,based on the Interferometric and Doppler techniques.In this paper,the horizontal wind field,gravity wave(GW)disturbance variance,and GW fluxes are analyzed through the meteor radar observation from 2012−2022,at Mohe(53.5°N,122.4°E)and Zuoling(30.5°N,114.6°E)stations of the(Chinese)Meridian Project.The Lomb−Scargle periodogram method has been utilized to analyze the periodic variations for time series with observational data gaps.The results show that the zonal winds at both stations are eastward dominated,while the meridional winds are southward dominated.The variance of GW disturbances in the zonal and meridional directions increases gradually with height,and there is a strong pattern of annual variation.The zonal momentum flux of GW changes little with height,showing weak annual variation.The meridional GW flux varies gradually from northward to southward with height,and the annual periodicity is stronger.For both stations,the maximum values of zonal and meridional wind occur close to the peak heights of GW flux,with opposite directions.This observational evidence is consistent with the filtering theory.The horizontal wind velocity,GW flux,and disturbance variance of the GW at Mohe are overall smaller than those at Zuoling,indicating weaker activities in the MLT at Mohe.The power spectral density(PSD)calculated by the Lomb−Scargle periodogram shows that there are 12-month period and 6-month period in horizontal wind field,GW disturbance variance and GW flux at both stations,and especially there is also a 4-month cycle in the disturbance variance.The PSD of the 12-month and 6-month cycles exhibits maximum values below 88 km and above 94 km.展开更多
Accurate knowledge of mesospheric winds and waves is essential for studying the dynamics and climate in the mesosphere and lower thermosphere(MLT)region.In this study,we conduct a comparative analysis of the mesospher...Accurate knowledge of mesospheric winds and waves is essential for studying the dynamics and climate in the mesosphere and lower thermosphere(MLT)region.In this study,we conduct a comparative analysis of the mesosphere tidal results obtained from two adjacent meteor radars at low latitudes in Kunming,China,from November 2013 to December 2014.These two radars operate at different frequencies of 37.5 MHz and 53.1 MHz,respectively.However,overall good agreement is observed between the two radars in terms of horizontal winds and tide observations.The results show that the dominant tidal waves of the zonal and meridional winds are diurnal and semidiurnal tides.Moreover,we conduct an exhaustive statistical analysis to compare the tidal amplitudes and vertical wavelengths recorded by the dual radar systems,which reveals a high degree of alignment in tidal dynamics.The investigation includes variances and covariances of tidal amplitudes,which demonstrate remarkable consistency across measurements from both radars.This finding highlights clear uniformity in the mesospheric tidal patterns observed at low latitudes by the two neighboring meteor radars.Results of the comparative analysis specifically underscore the significant correlation in vertical wavelength measurements,validating the robustness of radar observations for tidal research.展开更多
Nonperiodic interrupted sampling repeater jamming(ISRJ)against inverse synthetic aperture radar(ISAR)can obtain two-dimensional blanket jamming performance by joint fast and slow time domain interrupted modulation,whi...Nonperiodic interrupted sampling repeater jamming(ISRJ)against inverse synthetic aperture radar(ISAR)can obtain two-dimensional blanket jamming performance by joint fast and slow time domain interrupted modulation,which is obviously dif-ferent from the conventional multi-false-target deception jam-ming.In this paper,a suppression method against this kind of novel jamming is proposed based on inter-pulse energy function and compressed sensing theory.By utilizing the discontinuous property of the jamming in slow time domain,the unjammed pulse is separated using the intra-pulse energy function diffe-rence.Based on this,the two-dimensional orthogonal matching pursuit(2D-OMP)algorithm is proposed.Further,it is proposed to reconstruct the ISAR image with the obtained unjammed pulse sequence.The validity of the proposed method is demon-strated via the Yake-42 plane data simulations.展开更多
Cognitive bias,stemming from electronic measurement error and variability in human perception,exists in cognitive electronic warfare and affects the outcomes of conflicts.In this paper,the dynamic game approach is emp...Cognitive bias,stemming from electronic measurement error and variability in human perception,exists in cognitive electronic warfare and affects the outcomes of conflicts.In this paper,the dynamic game approach is employed to develop a model for cognitive bias induced by incomplete information and measurement errors in cognitive radar countermeasures.The payoffs for both parties are calculated using the radar's anti-jamming strategy matrix A and the jammer's jamming strategy matrix B.With perfect Bayesian equilibrium,a dynamic radar countermeasure model is established,and the impact of cognitive bias is analyzed.Drawing inspiration from the cognitive bias analysis method used in stock market trading,a cognitive bias model for cognitive radar countermeasures is introduced,and its correctness is mathematically proved.A gaming scenario involving the AN/SPY-1 radar and a smart jammer is set up to analyze the influence of cognitive bias on game outcomes.Simulation results validate the effectiveness of the proposed method.展开更多
Automotive radar has emerged as a critical component in Advanced Driver Assistance Systems(ADAS)and autonomous driving,enabling robust environmental perception through precise range-Doppler and angular measurements.It...Automotive radar has emerged as a critical component in Advanced Driver Assistance Systems(ADAS)and autonomous driving,enabling robust environmental perception through precise range-Doppler and angular measurements.It plays a pivotal role in enhancing road safety by supporting accurate detection and localization of surrounding objects.However,real-world deployment of automotive radar faces significant challenges,including mutual interference among radar units and dense clutter due to multiple dynamic targets,which demand advanced signal processing solutions beyond conventional methodologies.This paper presents a comprehensive review of traditional signal processing techniques and recent advancements specifically designed to address contemporary operational challenges in automotive radar.Emphasis is placed on direction-of-arrival(DoA)estimation algorithms such as Bartlett beamforming,Minimum Variance Distortionless Response(MVDR),Multiple Signal Classification(MUSIC),and Estimation of Signal Parameters via Rotational Invariance Techniques(ESPRIT).Among these,ESPRIT offers superior resolution for multi-target scenarios with reduced computational complexity compared to MUSIC,making it particularly advantageous for real-time applications.Furthermore,the study evaluates state-of-the-art tracking algorithms,including the Kalman Filter(KF),Extended KF(EKF),Unscented KF,and Bayesian filter.EKF is especially suitable for radar systems due to its capability to linearize nonlinear measurement models.The integration of machine learning approaches for target detection and classification is also discussed,highlighting the trade-off between the simplicity of implementation in K-Nearest Neighbors(KNN)and the enhanced accuracy provided by Support Vector Machines(SVM).A brief overview of benchmark radar datasets,performance metrics,and relevant standards is included to support future research.The paper concludes by outlining ongoing challenges and identifying promising research directions in automotive radar signal processing,particularly in the context of increasingly complex traffic scenarios and autonomous navigation systems.展开更多
基金National Key Research and Development Program of China (2023YFB3905801)。
文摘Accurate calibration of China's new generation ground-based polarimetric radar(GR) network is crucial yet challenging. Although application of the Dual-frequency Precipitation Radar(DPR) of the Global Precipitation Measurement Core Observatory for GR assessment is well-established, current methodologies are inherently limited. Focusing on three GRs in the Guangdong-Hong Kong-Macao Greater Bay Area(GBA)—strategically selected for their high overlapping coverage(>65%) and distinct from single GR or less dense coverage studies—this work introduces key refinements by integrating innovative enhancements into the volume-matching method(VMM), reflecting a systematic approach to mitigating potential error sources. Specifically, we integrate: 1) a novel frequency correction method that adapts to DPR-observed precipitation phase and type, replacing assumption-based polynomial fitting;and 2) a precise beam time-difference matching approach(accuracy < 1 s) to minimize temporal mismatch errors, which improves upon coarser time averaging methods. Furthermore, we developed statistically robust, optimized threshold criteria based on systematic sensitivity analyses using 11 quality control factors, including precipitation type, bright band effects, and attenuation correction limitations. These criteria establish an enhanced protocol designed to minimize errors arising from instrumental, frequency, and scanning differences. Application of this enhanced methodology to the GBA GRs(2021–2023) yielded a significantly improved matching accuracy(correlation coefficient, CC: 0.90–0.95;standard deviation,STD: 1.2–1.6 dB). A unique contribution of this work is the quantitative estimation of historical calibration errors and operational stability, which was achieved by linking VMM results with operational GR calibration and maintenance records. This analysis revealed decreasing STD trends and identified specific calibration-related events, such as an underestimation of approximately 2.43 dB for the Shenzhen radar following calibration in 2023. Consequently, the refined methodology provides a robust framework for ongoing GR network monitoring and offers a validated pathway for authenticating China's Fengyun-3G(FY-3G) satellite precipitation measurement radar(PMR) data.
基金supported by the National Key R&D Program of China(2024YFB2605500)the National Natural Science Foundation of China(52308444)the Fundamental Research Funds for the Central Universities(2242024K40036).
文摘In situ density and moisture content of asphalt pavement are essential controlling parameters that require accurate measurement for quality control and quality assurance purposes.The ground-penetrating radar(GPR)technique could provide non-destructive,non-contact,and full-coverage estimations of pavement density and moisture content.However,the technical readiness and drawbacks,including prediction models,signal processing algorithms,and testing hardware,remain unclear for agencies and construction practitioners,impeding large-scale implementations.This paper aims to provide a thorough review of the theoretical background and current practices of using GPR for non-destructive measurements of asphalt pavement density and moisture content during construction,thereby allowing for real-time correction of over-or under-compaction on site.The principles and applications of GPR-based density and moisture content prediction models were comprehensively summarized.Their strengths and limitations were discussed.Cutting-edge GPR equipment suitable for such applications was introduced,including their system components,application scenarios,and inherent limitations.Factors affecting prediction accuracy were analyzed.Advanced signal processing algorithms were discussed in the end,along with the in-place calibration procedure for aggregate dielectric constants.The reviewed technique could be a guiding tool for real-time monitoring of asphalt pavement density and moisture content using GPR,offering practical insights for future development and standardized deployment in construction quality management.
基金supported by the National Natural Science Foundation of China(62371155)the Heilongjiang Outstanding Youth Science Fund(JQ2022F002)。
文摘This paper addresses weak target detection problem for bistatic radar via a pseudo-spectrum(PS)based track-before-detect(TBD).Generally,PS-TBD estimates target position and velocity by means of pseudo-spectrum construction in the discrete measurement space and accurate energy accumulation in mixed coordinates.However,the grids within the polar sensing region of the receivers in the bistatic radar are not aligned.Traditional PS-TBD can not directly process these measurements.In this paper,a PS-TBD method for bistatic radar is proposed to overcome this problem.Each cell in the measurement space of the receivers is mapped to the aligned Cartesian coordinates and predicted to the integration frame according to the assumed filter velocity.A PS is formulated centered on the predicted Cartesian position.Then the samples of the pseudo-spectra are accumulated to the nearest cell around the predicted Cartesian position.The procedure of the energy integration is derived in detail.Simulation results validate the efficacy of the proposed method in terms of detection accuracy and parameter estimation.
文摘In high-intensity electromagnetic warfare,radar systems are persistently subjected to multi-jammer attacks,including potentially novel unknown jamming types that may emerge exclusively under wartime conditions.These jamming signals severely degrade radar detection performance.Precise recognition of these unknown and compound jamming signals is critical to enhancing the anti-jamming capabilities and overall reliability of radar systems.To address this challenge,this article proposes a novel open-set compound jamming cognition(OSCJC)method.The proposed method employs a detection-classification dual-network architecture,which not only overcomes the false alarm and misdetection issues of traditional closed-set recognition methods when dealing with unknown jamming but also effectively addresses the performance bottleneck of existing open-set recognition techniques focusing on single jamming scenarios in compound jamming environments.To achieve unknown jamming detection,we first employ a consistency labeling strategy to train the detection network using diverse known jamming samples.This strategy enables the network to acquire highly generalizable jamming features,thereby accurately localizing candidate regions for individual jamming components within compound jamming.Subsequently,we introduce contrastive learning to optimize the classification network,significantly enhancing both intra-class clustering and inter-class separability in the jamming feature space.This method not only improves the recognition accuracy of the classification network for known jamming types but also enhances its sensitivity to unknown jamming types.Simulations and experimental data are used to verify the effectiveness of the proposed OSCJC method.Compared with the state-of-the-art open-set recognition methods,the proposed method demonstrates superior recognition accuracy and enhanced environmental adaptability.
基金supported by the National Natural Science Foundation of China(62271247)the Natural Science Foundation of Jiangsu Province(BK20240181)+4 种基金the Dreams Foundation of Jianghuai Advance Technology Center(2023-ZM01D001)the National Aerospace Science Foundation of China(20220055052001)the Qing Lan Project of Jiangsu Provincethe Fund of Prospective Layout of Scientific Research for Nanjing University of Aeronautics and Astronauticsthe Key Laboratory of Radar Imaging and Microwave Photonics(Nanjing University of Aeronautics and Astronautics),Ministry of Education。
文摘In this paper,the joint design of transmit and receive beamformers for transmit subaperturing multiple-input-multiple-output(TS-MIMO)radar is investigated,aiming to enhance its low probability of intercept(LPI)capability.The main objective is to simultaneously minimize the transmission power,suppress the transmit sidelobe levels,and minimize the probability of intercept,thus bolstering the LPI performance of the radar system while maintaining the desired target detection performance.An alternative optimization method is proposed to jointly optimize the transmit and receive beamformers,yielding an unified LPI optimization framework.Particularly,the proposed iterative algorithm based on the Lagrange duality theory for transmit beamforming is more efficient than the conventional convex optimization method.Numerical experiments highlight the effectiveness of the proposed approach in sidelobe suppression and computational efficiency.
基金funded by Princess Nourah bint Abdulrahman University Researchers Support-ing Project number(PNURSP2026R346)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Motor imbalance is a critical failure mode in rotating machinery,potentially causing severe equipment damage if undetected.Traditional vibration-based diagnostic methods rely on direct sensor contact,leading to installation challenges and measurement artifacts that can compromise accuracy.This study presents a novel radar-based framework for non-contact motor imbalance detection using 24 GHz continuous-wave radar.A dataset of 1802 experimental trials was sourced,covering four imbalance levels(0,10,20,30 g)across varying motor speeds(500–1500 rpm)and load torques(0–3 Nm).Dual-channel in-phase and quadrature radar signals were captured at 10,000 samples per second for 30-s intervals,preserving both amplitude and phase information for analysis.A multi-domain feature extraction methodology captured imbalance signatures in time,frequency,and complex signal domains.From 65 initial features,statistical analysis using Kruskal–Wallis tests identified significant descriptors,and recursive feature elimination with Random Forest reduced the feature set to 20 dimensions,achieving 69%dimensionality reduction without loss of performance.Six machine learning algorithms,Random Forest,Extra Trees Classifier,Extreme Gradient Boosting,Categorical Boosting,Support Vector Machine with radial basis function kernel,and k-Nearest Neighbors were evaluated with grid-search hyperparameter optimization and five-fold cross-validation.The Extra Trees Classifier achieved the best performance with 98.52%test accuracy,98%cross-validation accuracy,and minimal variance,maintaining per-class precision and recall above 97%.Its superior performance is attributed to its randomized split selection and full bootstrapping strategy,which reduce variance and overfitting while effectively capturing the nonlinear feature interactions and non-normal distributions present in the dataset.The model’s average inference time of 70 ms enables near real-time deployment.Comparative analysis demonstrates that the radar-based framework matches or exceeds traditional contact-based methods while eliminating their inherent limitations,providing a robust,scalable,and noninvasive solution for industrial motor condition monitoring,particularly in hazardous or space-constrained environments.
基金supported by the National Natural Science Foundation of China(Nos.62071482 and 62471348)the Shaanxi Association of Science and Technology Youth Talent Support Program Project,China(No.20230137)+1 种基金the Innovative Talents Cultivate Program for Technology Innovation Team of Shaanxi Province,China(No.2024RS-CXTD-08)the Youth Innovation Team of Shaanxi Universities,China。
文摘Within the domain of Intelligent Group Systems(IGSs),this paper develops a resourceaware multitarget Constant False Alarm Rate(CFAR)detection framework for multisite MIMO radar systems.It underscores the necessity of managing finite transmit and receive antennas and transmit power systematically to enhance detection performance.To tackle the multidimensional resource optimization challenge,we introduce a Cooperative Transmit-Receive Antenna Selection and Power Allocation(CTRSPA)strategy.It employs a perception-action cycle that incorporates uncertain external support information to optimize worst-case detection performance with multiple targets.First,we derive a closed-form expression that incorporates uncertainty for the noncoherent integration squared-law detection probability using the Neyman-Pearson criterion.Subsequently,a joint optimization model for antenna selection and power allocation in CFAR detection is formulated,incorporating practical radar resource constraints.Mathematically,this represents an NPhard problem involving coupled continuous and Boolean variables.We propose a three-stage method—Reformulation,Node Picker,and Convex Power Allocation—that capitalizes on the independent convexity of the optimization model for each variable,ensuring a near-optimal result.Simulations confirm the approach's effectiveness,efficiency,and timeliness,particularly for large-scale radar networks,and reveal the impact of threat levels,system layout,and detection parameters on resource allocation.
基金funded by the National Key Research and Development Program of China(Grant No.2023YFB3907500)the National Natural Science Foundation(Grant No.42330602)the“Fengyun Satellite Remote Sensing Product Validation and Verification”Youth Innovation Team of the China Meteorological Administration(Grant No.CMA2023QN12)。
文摘The Global Precipitation Measurement(GPM)dual-frequency precipitation radar(DPR)products(Version 07A)are employed for a rigorous comparative analysis with ground-based operational weather radar(GR)networks.The reflectivity observed by GPM Ku PR is compared quantitatively against GR networks from CINRAD of China and NEXRAD of the United States,and the volume matching method is used for spatial matching.Additionally,a novel frequency correction method for all phases as well as precipitation types is used to correct the GPM Ku PR radar frequency to the GR frequency.A total of 20 GRs(including 10 from CINRAD and 10 from NEXRAD)are included in this comparative analysis.The results indicate that,compared with CINRAD matched data,NEXRAD exhibits larger biases in reflectivity when compared with the frequency-corrected Ku PR.The root-mean-square difference for CINRAD is calculated at 2.38 d B,whereas for NEXRAD it is 3.23 d B.The mean bias of CINRAD matched data is-0.16 d B,while the mean bias of NEXRAD is-2.10 d B.The mean standard deviation of bias for CINRAD is 2.15 d B,while for NEXRAD it is 2.29 d B.This study effectively assesses weather radar data in both the United States and China,which is crucial for improving the overall consistency of global precipitation estimates.
基金supported by the National Nat-ural Science Foundation of China(No.42376174)the Natural Science Foundation of Shanghai(No.23ZR 1426900).
文摘Synthetic aperture radar(SAR)aboard SEASAT was first launched in 1978.At the beginning of the 21st century,the Chinese remote sensing community recognized the urgent need to develop domestic SAR capabilities.Unlike scatterometers and al-timeters,space-borne SAR offers high-resolution images of the ocean,regardless of weather conditions or time of day.SAR imagery provides rich information about the sea surface,capturing complicated dynamic processes in the upper layers of the ocean,particular-ly in relation to tropical cyclones.Over the past four decades,the advantages of SAR have been increasingly recognized,leading to notable marine applications,especially in the development of algorithms for retrieving wind and wave data from SAR images.This study reviews the history,progress,and future outlook of SAR-based monitoring of sea surface wind and waves.In particular,the ap-plicability of various SAR wind and wave algorithms is systematically investigated,with a particular focus on their performance un-der extreme sea conditions.
基金supported by the National Natural Science Foundation of China(Grant Nos.62371191 and 62401207)the Space Optoelectronic Measurement and Perception Laboratory,Beijing Institute of Control Engineering(Grant No.LabSOMP-2023-05)+1 种基金the China Postdoctoral Science Foundation(Grant No.2024M764276)the Science and Technology Commission of Shanghai Municipality(Grant No.22DZ2229004).
文摘A microwave photonic prototype for concurrent radar detection and spectrum sensing is proposed.A direct digital synthesizer and an analog electronic circuit are integrated to generate an intermediate frequency(IF)linearly frequency-modulated(LFM)signal ranging from 2.5 to 9.5 GHz,with an instantaneous bandwidth of 1 GHz.The IF LFM signal is converted to the optical domain via an intensity modulator and filtered by a fiber Bragg grating to generate two second-order sidebands.The two sidebands beat each other to generate a frequency-and-bandwidth-quadrupled LFM signal.By changing the center frequency of the IF LFM signal,the radar function can be operated within 8 to 40 GHz.One second-order sideband works in conjunction with the stimulated Brillouin scattering gain spectrum for microwave frequency measurement,providing an instantaneous measurement bandwidth of 2 GHz and a frequency measurement range from 0 to 40 GHz.The prototype is demonstrated to be capable of achieving a range resolution of 3.75 cm,a range error of less than ±2 cm,a radial velocity error within ±1 cm∕s,delivering clear imaging of multiple small targets,and maintaining a frequency measurement error of less than ±7 MHz and a frequency resolution of better than 20 MHz.
基金supported by the National Natural Science Foundation of China under Grant No.62271043Ministry of Education of China under Grant No.8091B032123Beijing Natural Science Foundation under Grant No.L212029。
文摘Radar cross section(RCS)plays a critical role in modeling target scattering characteristics and enhancing the precision of target detection and localization in integrated sensing and communication(ISAC)systems.This paper investigates the human body RCS at 26 GHz via multiangle channel measurements under different clothing conditions.Based on calibrated electromagnetic(EM)parameters,the RCS characteristics of the human body in far-field conditions are analyzed using ray-tracing(RT)simulations.Some suggestions for the design of ISAC systems are also discussed.The results provide a solid theoretical foundation and practical reference for the modeling of target scattering characteristics for ISAC channels.
基金supported by the Fundamental Research Funds for the Central Universities,CHD(NO.300102263205 and NO.300102264916)the West Light Cross-Disciplinary Innovation team of Chinese Academy of Sciences(NO.E1294301).supported by the Fundamental Research Funds for the Central Universities,CHD(NO.300102263205 and NO.300102264916)the West Light Cross-Disciplinary Innovation team of Chinese Academy of Sciences(NO.E1294301).
文摘The meteor radar can detect the zenith angle,azimuth,radial velocity,and altitude of meteor trails so that one can invert the wind profiles in the mesosphere and low thermosphere(MLT)region,based on the Interferometric and Doppler techniques.In this paper,the horizontal wind field,gravity wave(GW)disturbance variance,and GW fluxes are analyzed through the meteor radar observation from 2012−2022,at Mohe(53.5°N,122.4°E)and Zuoling(30.5°N,114.6°E)stations of the(Chinese)Meridian Project.The Lomb−Scargle periodogram method has been utilized to analyze the periodic variations for time series with observational data gaps.The results show that the zonal winds at both stations are eastward dominated,while the meridional winds are southward dominated.The variance of GW disturbances in the zonal and meridional directions increases gradually with height,and there is a strong pattern of annual variation.The zonal momentum flux of GW changes little with height,showing weak annual variation.The meridional GW flux varies gradually from northward to southward with height,and the annual periodicity is stronger.For both stations,the maximum values of zonal and meridional wind occur close to the peak heights of GW flux,with opposite directions.This observational evidence is consistent with the filtering theory.The horizontal wind velocity,GW flux,and disturbance variance of the GW at Mohe are overall smaller than those at Zuoling,indicating weaker activities in the MLT at Mohe.The power spectral density(PSD)calculated by the Lomb−Scargle periodogram shows that there are 12-month period and 6-month period in horizontal wind field,GW disturbance variance and GW flux at both stations,and especially there is also a 4-month cycle in the disturbance variance.The PSD of the 12-month and 6-month cycles exhibits maximum values below 88 km and above 94 km.
基金supported by the National Natural Science Foundation of China (Grant Nos. 42125402 and 42174183)the National Key Technologies R&D Program of China (Grant No.2022YFF0503703)+2 种基金the B-type Strategic Priority Program of the Chinese Academy of Sciences (Grant No. XDB41000000)the foundation of the National Key Laboratory of Electromagnetic Environment and the Fundamental Research Funds for the Central Universitiesthe Chinese Meridian Project
文摘Accurate knowledge of mesospheric winds and waves is essential for studying the dynamics and climate in the mesosphere and lower thermosphere(MLT)region.In this study,we conduct a comparative analysis of the mesosphere tidal results obtained from two adjacent meteor radars at low latitudes in Kunming,China,from November 2013 to December 2014.These two radars operate at different frequencies of 37.5 MHz and 53.1 MHz,respectively.However,overall good agreement is observed between the two radars in terms of horizontal winds and tide observations.The results show that the dominant tidal waves of the zonal and meridional winds are diurnal and semidiurnal tides.Moreover,we conduct an exhaustive statistical analysis to compare the tidal amplitudes and vertical wavelengths recorded by the dual radar systems,which reveals a high degree of alignment in tidal dynamics.The investigation includes variances and covariances of tidal amplitudes,which demonstrate remarkable consistency across measurements from both radars.This finding highlights clear uniformity in the mesospheric tidal patterns observed at low latitudes by the two neighboring meteor radars.Results of the comparative analysis specifically underscore the significant correlation in vertical wavelength measurements,validating the robustness of radar observations for tidal research.
基金supported by the National Natural Science Foundation of China(62001481,61890542,62071475)the Natural Science Foundation of Hunan Province(2022JJ40561)the Research Program of National University of Defense Technology(ZK22-46).
文摘Nonperiodic interrupted sampling repeater jamming(ISRJ)against inverse synthetic aperture radar(ISAR)can obtain two-dimensional blanket jamming performance by joint fast and slow time domain interrupted modulation,which is obviously dif-ferent from the conventional multi-false-target deception jam-ming.In this paper,a suppression method against this kind of novel jamming is proposed based on inter-pulse energy function and compressed sensing theory.By utilizing the discontinuous property of the jamming in slow time domain,the unjammed pulse is separated using the intra-pulse energy function diffe-rence.Based on this,the two-dimensional orthogonal matching pursuit(2D-OMP)algorithm is proposed.Further,it is proposed to reconstruct the ISAR image with the obtained unjammed pulse sequence.The validity of the proposed method is demon-strated via the Yake-42 plane data simulations.
文摘Cognitive bias,stemming from electronic measurement error and variability in human perception,exists in cognitive electronic warfare and affects the outcomes of conflicts.In this paper,the dynamic game approach is employed to develop a model for cognitive bias induced by incomplete information and measurement errors in cognitive radar countermeasures.The payoffs for both parties are calculated using the radar's anti-jamming strategy matrix A and the jammer's jamming strategy matrix B.With perfect Bayesian equilibrium,a dynamic radar countermeasure model is established,and the impact of cognitive bias is analyzed.Drawing inspiration from the cognitive bias analysis method used in stock market trading,a cognitive bias model for cognitive radar countermeasures is introduced,and its correctness is mathematically proved.A gaming scenario involving the AN/SPY-1 radar and a smart jammer is set up to analyze the influence of cognitive bias on game outcomes.Simulation results validate the effectiveness of the proposed method.
基金supported in part by the National Science and Technology Council,Taiwan:NSTC 113-2410-H-030-077-MY2.
文摘Automotive radar has emerged as a critical component in Advanced Driver Assistance Systems(ADAS)and autonomous driving,enabling robust environmental perception through precise range-Doppler and angular measurements.It plays a pivotal role in enhancing road safety by supporting accurate detection and localization of surrounding objects.However,real-world deployment of automotive radar faces significant challenges,including mutual interference among radar units and dense clutter due to multiple dynamic targets,which demand advanced signal processing solutions beyond conventional methodologies.This paper presents a comprehensive review of traditional signal processing techniques and recent advancements specifically designed to address contemporary operational challenges in automotive radar.Emphasis is placed on direction-of-arrival(DoA)estimation algorithms such as Bartlett beamforming,Minimum Variance Distortionless Response(MVDR),Multiple Signal Classification(MUSIC),and Estimation of Signal Parameters via Rotational Invariance Techniques(ESPRIT).Among these,ESPRIT offers superior resolution for multi-target scenarios with reduced computational complexity compared to MUSIC,making it particularly advantageous for real-time applications.Furthermore,the study evaluates state-of-the-art tracking algorithms,including the Kalman Filter(KF),Extended KF(EKF),Unscented KF,and Bayesian filter.EKF is especially suitable for radar systems due to its capability to linearize nonlinear measurement models.The integration of machine learning approaches for target detection and classification is also discussed,highlighting the trade-off between the simplicity of implementation in K-Nearest Neighbors(KNN)and the enhanced accuracy provided by Support Vector Machines(SVM).A brief overview of benchmark radar datasets,performance metrics,and relevant standards is included to support future research.The paper concludes by outlining ongoing challenges and identifying promising research directions in automotive radar signal processing,particularly in the context of increasingly complex traffic scenarios and autonomous navigation systems.
