This study presents finely resolved radar signatures of multiple cyclonic vortices associated with an EF2 tornadic supercell that occurred in Guangzhou on 16 June 2022 and discusses how the mesocyclone formed on the l...This study presents finely resolved radar signatures of multiple cyclonic vortices associated with an EF2 tornadic supercell that occurred in Guangzhou on 16 June 2022 and discusses how the mesocyclone formed on the lee side of mountain.A nearby X-band phased-array radar provides evidence that the mesocyclone was shallow,with a depth generally confined to less than 3 km.The mesocyclonic feature was observed to initiate from near-ground level,driven by the interaction between intensifying cold pool surges and shallow lee-side ambient flows.It was first recognized shortly after the presence of near-ground cyclonic convergence signatures over the leading edges of cold pool outflows.Over the subsequent 17 min,the mesocyclone developed upward,reaching a maximum height of 3 km,and produced a tornado 8min later.Nearly coinciding with the time of tornadogenesis,a noticeable separation of the low-level tornado cyclone from the midlevel mesocyclone was observed.This shift in the vertically oriented vortex tube was likely caused by modifications to the low-level flow due to the complex hilly terrain or by occlusions associated with rear-flank downdrafts.After tornadogenesis,high-resolution X-PAR observations revealed that the lowest-level mesocyclonic signature contracted into a gate-to-gate tornadic vortex signature(TVS)at the tip of hook echoes.Compared to conventional S-band operational weather radars,rapid-scan X-PAR observations indicate that a core diameter threshold of 1.5–2 km could be employed to identify a cyclonically sheared radial velocity couplet as a TVS,potentially extending the lead time for Doppler-based tornado warnings.展开更多
In September 2020,a pioneering observational network of three X-band phased-array radars(XPARs)was established in Xiamen,a subtropical coastal and densely populated city in southeastern China.Statistically,this study ...In September 2020,a pioneering observational network of three X-band phased-array radars(XPARs)was established in Xiamen,a subtropical coastal and densely populated city in southeastern China.Statistically,this study demonstrated that the XPAR network outperforms single S-band radar in revealing the warm-season convective storms in Xiamen in a fine-scale manner.The findings revealed that convective activity in Xiamen is most frequent in the central and northern mountainous regions,with lower frequency observed in the southern coastal areas.The diurnal pattern of convection occurrence exhibited a unimodal distribution,with a peak in the afternoon.The frequent occurrence of convective storms correlates well in both time and space with the active terrain uplift that occurs when the prevailing winds encounter mountainous areas.Notably,September stands apart with a bimodal diurnal pattern,featuring a prominent afternoon peak and a significant secondary peak before midnight.Further examination of dense rain gauge data in Xiamen indicates that high-frequency areas of short-duration heavy rainfall largely coincide with regions of active convective storms,except for a unique rainfall hotspot in southern Xiamen,where moderate convection frequency is accompanied by substantial rainfall.This anomalous rainfall,predominantly nocturnal,appears less influenced by terrain uplift and exhibits higher precipitation efficiency than daytime rainfall.These preliminary findings offer insights into the characteristics of convection occurrence in Xiamen's subtropical coastal environment and hold promise for enhancing the accuracy of convection and precipitation forecasts in similar environments.展开更多
To verify the detection capability of X-band dual-polarization phased-array radar for forest fires,this paper utilizes X-band dual-polarization phased-array radar data,Himawari-8 satellite data,combined with ground me...To verify the detection capability of X-band dual-polarization phased-array radar for forest fires,this paper utilizes X-band dual-polarization phased-array radar data,Himawari-8 satellite data,combined with ground meteorological automatic station data.A case study of a forest fire in Ao Feng Mountain on February 19,2021,was conducted to comparatively analyze the monitoring results from these two remote sensing methods.The results show that both methods exhibit significant features associated with the forest fire process observed and are effective modern methods of forest fire monitoring.The Himawari-8 satellite identified the fire point at 07:10(LST;LST=UTC+8)with subsequent observations every 10 minutes until 10:00,nearly two hours before the fire was fully extinguished.Compared with the satellite,the Xband dual polarization phased array radar detectedthe fire 14 minutes earlier,with an improved temporal resolution of one minute,and was not affected by cloud cover.In the triggering stage,vigorous stage,sustained burning stage,and extinguishing stage of the forest fire,radar characteristic factors including reflectivity(Z),differential reflectivity(ZDR),and correlation coefficient(CC)showed strong correlations with the fire progression.The radar monitoring results were continuous,complete,and precise.In summary,the X-band dual-polarization phased-array radar offers more detailed detection information,shorter detection time interval,and higher detection spatial accuracy.It presents a promising new method for forest fire detection,providing crucial guidance for on-site rescue operations,particularly for small-scale fire events.展开更多
The strong destructive winds during tornadoes can greatly threaten human life and destroy property.The increasing availability of visual and remote observations,especially by Doppler weather radars,is of great value i...The strong destructive winds during tornadoes can greatly threaten human life and destroy property.The increasing availability of visual and remote observations,especially by Doppler weather radars,is of great value in understanding tornado formation and issuing warnings to the public.In this study,we present the first documented tornado over water detected by a state-of-the-art dual-polarization phased-array radar(dual-PAR)in China.In contrast to new-generation weather radars,the dual-PAR shows great advantages in tornado detection for its high spatial resolution,reliable polarimetric variables,and rapid-scan strategy.The polarimetric signature of copolar cross-correlation coefficient with anomalously low magnitude appears to be effective for verifying a tornado and thus is helpful for issuing tornado warnings.The Guangdong Meteorological Service has been developing an experimental X-band dual-PAR network in the Pearl River Delta with the goal of deploying at least 40 advanced dual-PARs and other dual-polarization weather radars before 2035.This network is the first quasi-operational X-band dual-PAR network with unprecedented high coverage in the globe.With such high-performance close-range PARs,efficient operational nowcasting and warning services for small-scale,rapidly evolving,and damaging weather(e.g.,tornadoes,localized heavy rainfall,microbursts,and hail)can be expected.展开更多
An X-band phased-array meteorological radar （XPAR） was developed in China and will be installed in an airplane to observe precipitation systems for research purposes.In order to examine the observational capability ...An X-band phased-array meteorological radar （XPAR） was developed in China and will be installed in an airplane to observe precipitation systems for research purposes.In order to examine the observational capability of the XPAR and to test the operating mode and calibration before installation in the airplane,a mobile X-band Doppler radar （XDR） and XPAR were installed at the same site to observe convective precipitation events.Nearby S-band operational radar （SA） data were also collected to examine the reflectivity bias of XPAR.An algorithm for quantitative analysis of reflectivity and velocity differences and radar sensitivity of XPAR is presented.The reflectivity and velocity biases of XPAR are examined with SA and XDR.Reflectivity sensitivities,the horizontal and vertical structures of reflectivity by the three radars are compared and analyzed.The results indicated that while the XPRA with different operating modes can capture the main characteristic of 3D structures of precipitation,and the averaged reflectivity differences between XPAR and XDR,and XDR and SA,were 0.4 dB and 6.6 dB on 13 July and-4.5 dB and 5.1 dB on 2 August 2012,respectively.The minimum observed reflectivities at a range of 50 km for XPAR,XDR and SA were about 15.4 dBZ,13.5 dBZ and-3.5 dBZ,respectively.The bias of velocity between XPAR and XDR was negligible.This study provides a possible method for the quantitative comparison of the XPAR data,as well as the sensitivity of reflectivity,calibration,gain and bias introduced by pulse compression.展开更多
A novel adaptive sampling interval algorithm for multitarget tracking is presented. This algorithm which is based on interacting multiple models incorporates the grey relational grade （GRG） into the particle swarm o...A novel adaptive sampling interval algorithm for multitarget tracking is presented. This algorithm which is based on interacting multiple models incorporates the grey relational grade （GRG） into the particle swarm optimization （PSO）. Firstly, the desired tracking accuracy is set for each target. Secondly, sampling intervals are selected as particles, and then the advantage of the GRG is taken as the measurement function for resource management. Meanwhile, the fitness value of the PSO is used to measure the difference between desired tracking accuracy and estimated tracking accuracy. Finally, it is suggested that the radar should track the target whose prediction value of the next sampling interval is the smallest. Simulations show that the proposed method improves both the tracking accuracy and tracking efficiency of the phased-array radar.展开更多
The performance of different quantitative precipitation estimation(QPE) relationships is examined using the polarimetric variables from the X-band polarimetric phased-array radars in Guangzhou,China.Three QPE approach...The performance of different quantitative precipitation estimation(QPE) relationships is examined using the polarimetric variables from the X-band polarimetric phased-array radars in Guangzhou,China.Three QPE approaches,namely,R(ZH),R(ZH,ZDR) and R(KDP),are developed for horizontal reflectivity,differential reflectivity and specific phase shift rate,respectively.The estimation parameters are determined by fitting the relationships to the observed radar variables using the T-matrix method.The QPE relationships were examined using the data of four heavy precipitation events in southern China.The examination shows that the R(ZH) approach performs better for the precipitation rate less than 5 mm h-1, and R(KDP) is better for the rate higher than 5 mm h-1, while R(ZH,ZDR) has the worst performance.An adaptive approach is developed by taking the advantages of both R(ZH) and R(KDP) approaches to improve the QPE accuracy.展开更多
Dual-Doppler radar detection and wind-field retrieval techniques are crucial for capturing small-scale structures within convective systems.The spatiotemporal resolution of radar data is a key factor influencing the a...Dual-Doppler radar detection and wind-field retrieval techniques are crucial for capturing small-scale structures within convective systems.The spatiotemporal resolution of radar data is a key factor influencing the accuracy of wind-field observations.Recently,an advanced X-band phased-array weather radar system was deployed in Foshan,Guangdong Province,China,comprising a central collaborative control unit and multiple networked phased-array radar front-ends.These radar front-ends work together to scan a common area,achieving a maximum data time difference of 5 s and a volume scan interval of 30 s,thereby providing three-dimensional wind-field data with higher spatiotemporal resolution and greater accuracy than achieved using traditional methods.This study utilized the X-band phased-array weather radar system to analyze the development of a substantial hailstorm that occurred over Foshan on 26 March 2022.Analysis indicated that hail cloud activity intensified considerably after 1442 local time,with the maximum reflectivity factor exceeding 60 dBZ above the altitude of the-20℃ level,and reflectivity continued to increase over the subsequent 12 min.More precise information on the flow-field structure of the storm was obtained by examining the X-band radar data.The temporal and vertical variations in the maximum reflectivity factor,updraft velocity,vertical wind shear,and horizontal wind speed within a hailstorm cloud were scrutinized.The results show that the altitude,intensity,and range of the main updraft area increased as the storm core ascended.Concurrently,the vertical wind shear at mid-lower levels of the storm became more pronounced as the altitude of the strong radar echo center increased prior to the peak of the updraft.Therefore,a new hail warning index was developed by using the vertical wind shear,and the index can be used to issue warnings up to 12 min earlier than achievable using traditional methods detecting increases in hailstorm intensity.展开更多
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
China launched its first spaceborne Precipitation Measurement Radar(PMR)on the FY-3G satellite in April 2023.To achieve the scientific goal of measuring the three-dimensional precipitation structure,evaluating the qua...China launched its first spaceborne Precipitation Measurement Radar(PMR)on the FY-3G satellite in April 2023.To achieve the scientific goal of measuring the three-dimensional precipitation structure,evaluating the quantitative measurement ability of the PMR is critical.China operates more than 250 weather radars over the mainland.Consistency of the spaceborne radar with ground-based radars will enhance precipitation measurement ability,especially over oceans and mountains where observations are sparse.Additionally,the spaceborne radar can be used to evaluate the spatial and temporal homogeneity of the ground-based radar network.This paper focuses on comparing the PMR onboard the FY-3G satellite with S-band China New Generation Weather Radars(CINRADs).A comparison algorithm between the PMR and CINRADs has been developed,incorporating detailed quality control,attenuation correction,data optimization,spatiotemporal matching,non-uniform beam filling constraint,uniformity constraint,and frequency correction.The matched data in typical months of four seasons were selected to carry out the comparison.The data consistency between the PMR and CINRADs was analyzed.The correlation coefficient is 0.87,the deviation is 0.89 dB,and the standard deviation is 2.50 dB,based on 98226 matching samples.The results show the radar reflectivity of the PMR is quite comparable to that of the CINRADs,demonstrating that the PMR data quality is satisfactory and can be used to verify and correct data consistency among multiple ground-based radars.This work also paves the way for data fusion and joint application of satellite and ground radars in the future.展开更多
Dual-polarization(dual-pol)radar variables provide information about the quantity,type,size,and water content of hydrometeors.Assimilating these dual-pol radar variables into numerical weather prediction models can en...Dual-polarization(dual-pol)radar variables provide information about the quantity,type,size,and water content of hydrometeors.Assimilating these dual-pol radar variables into numerical weather prediction models can enhance forecast accuracy.Observation operators are essential for radar data assimilation.This study focuses on applying a realistic dual-pol radar observation operator to more accurately calculate dual-pol radar variables.Previously reported dual-pol radar observation operators tended to overestimate radar variables near 0℃ in convective precipitation and simulate unrealistic dual-pol radar variables in subfreezing regions.To address this,the improved operator(KNU dual-pol radar observation operator;K-DROP)limits the distribution of mixed-phase hydrometeors,which have both solid and liquid properties,in areas with strong updrafts and downdrafts,improving the overestimation of radar variables near the melting layer.Additionally,by applying the observed snow axis ratio during winter to K-DROP,the issue of differential reflectivity(Z_(DR))being calculated as a constant value in subfreezing regions has been improved.By incorporating the observed maximum radius of hydrometeors into K-DROP,the overestimation of reflectivity(Z_(H))in subfreezing regions,the overestimation of Z_(DR)in warm regions,and the underestimation of specific differential phase(K_(DP))in subfreezing regions and overestimation in warm regions,are improved.Compared to previous operators,the enhanced version reported in the present work produces more realistic dual-pol radar variables.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
FY-3G is the first polar-orbiting satellite equipped with a precipitation measurement radar(PMR)operating at Ku-andKa-band frequencies in China.In this study,we compare the reflectivity data from the FY-3G PMR Ku prod...FY-3G is the first polar-orbiting satellite equipped with a precipitation measurement radar(PMR)operating at Ku-andKa-band frequencies in China.In this study,we compare the reflectivity data from the FY-3G PMR Ku product and groundbasedradars(GRs)during 2024.Also,the FY-3G PMR is used as a third-party reference to evaluate the reflectivityconsistency among different GRs.The FY-3G PMR and GRs share similarities in their general distribution,characteristics,and intensity of reflectivity in strong precipitation cloud systems,though the former presents less detailed system structure.Systematic deviations between the FY-3G PMR and GRs and between GRs are comparable,albeit the reflectivity of the FY-3G PMR is generally slightly stronger than that of GRs(especially X-band GRs),with a mean bias ranging from 0.7 to 1.7dB.S-band GRs exhibit the smallest systematic deviation(STD=3.09 dB)from the FY-3G PMR,whereas the X-band GRsshow the largest(STD=3.61 dB),indirectly indicating the highest internal consistency among S-band GRs and the lowestamong X-band GRs.Besides,both S-and C-band GRs display similar deviations when paired with the FY-3G PMR as wellas when paired with their adjacent S/C-band GRs,suggesting good consistency between these two bands.In contrast,XbandGRs exhibit relatively poor consistency with S-band GRs and the FY-3G PMR,showing a deviation ranging from 3.0to 4.6 dB.展开更多
Ground-based radar is the primary means by which severe storms are monitored and tracked;however, due to limited coverage, important data is often missed over ocean and mountainous areas. On the other hand, geostation...Ground-based radar is the primary means by which severe storms are monitored and tracked;however, due to limited coverage, important data is often missed over ocean and mountainous areas. On the other hand, geostationary(GEO)weather satellites provide continuous observations with seamless coverage with advanced imager, despite their limited capability to penetrate clouds. Combining satellite and ground-radar observations could exploit the advantages of both techniques, providing tracking capability close to that of ground radar while maintaining full spatial coverage. This study presents a novel method called Multi-dimensional satellite Observation information for Radar Estimation(MORE) to reconstruct radar composite reflectivity(CREF). Deep learning techniques are important components of MORE for estimating CREF from China's Fengyun-4B(FY-4B) GEO satellite observations. Two models are developed: an infraredonly(IR-Single) model available for all times, and a visible-infrared(VIS+IR) model for daytime applications. These models incorporate multi-dimensional satellite observation information, including temporal, spatial, spectral, and viewing angle information, to enhance the accuracy of radar echo reconstruction. Results demonstrate that the VIS+IR model outperforms the IR-Single model, and both models achieves a root-mean-square error(RMSE) of less than 6 dBZ and a coefficient of determination(R~2) of greater than 0.7. The models effectively reconstruct radar echoes, including strong echoes exceeding 50 dBZ, and show good agreement with precipitation data in radar-blind areas. This study offers a valuable solution for severe weather monitoring and tracking in regions lacking ground-based radar observations, and provides a potential tool for enhanced data assimilation in numerical weather prediction(NWP) models.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
To address the issues of poor adaptability in resource allocation and low multi-agent cooperation efficiency in Joint Radar and Communication(JRC)systems under dynamic environments,an intelligent optimization framewor...To address the issues of poor adaptability in resource allocation and low multi-agent cooperation efficiency in Joint Radar and Communication(JRC)systems under dynamic environments,an intelligent optimization framework integrating Deep Reinforcement Learning(DRL)and Graph Neural Network(GNN)is proposed.This framework models resource allocation as a Partially Observable Markov Game(POMG),designs a weighted reward function to balance radar and communication efficiencies,adopts the Multi-Agent Proximal Policy Optimization(MAPPO)framework,and integrates Graph Convolutional Networks(GCN)and Graph Sample and Aggregate(Graph-SAGE)to optimize information interaction.Simulations show that,compared with traditional methods and pure DRL methods,the proposed framework achieves improvements in performance metrics such as communication success rate,Average Age of Information(AoI),and policy convergence speed,effectively enabling resource management in complex environments.Moreover,the proposed GNN-DRL-based intelligent optimization framework obtains significantly better performance for resource management in multi-agent JRC systems than traditional methods and pure DRL methods.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
Integrated sensing and communication(ISAC)is an appealing approach to address spectrum congestion and beamforming is an effective method to realize ISAC.In this paper,we investigate the beamforming design problem for ...Integrated sensing and communication(ISAC)is an appealing approach to address spectrum congestion and beamforming is an effective method to realize ISAC.In this paper,we investigate the beamforming design problem for multiple-input multipleoutput(MIMO)ISAC systems and propose to maximize the radar beampattern gain of the target direction while ensuring the signal-to-interference-plus-noise ratio(SINR)constraints of communication users.Particularly,we discuss two cases of ISAC transmit beamforming,i.e.,Case-Ⅰand Case-Ⅱ,which do not have and do have the dedicated probing signal,respectively.For these two cases of transmit beamforming design problems,we start from the single-user scenario and provide the closed-form solutions for MIMO ISAC beamforming vectors.Then,we consider the multiuser scenario and utilize the semidefinite relaxation technique to convert the beamforming design problems into convex semidefinite programming problems.Furthermore,we investigate the impact of the channel correlation between radar and communication on the performance gain of MIMO ISAC systems and characterize the performance tradeoff.Numerical results validate that the dedicated probing signal is unnecessary in the single-user scenario,whereas it has a slight improvement in target detection performance at low SINR thresholds in the multi-user scenario.It is also shown that the stronger the correlation between radar and communication channels,the greater the performance gain of the system.展开更多
基金supported by the National Key R&D Program of China(2022YFC3004101)the National Natural Science Foundation of China(Grant No.42275006)+2 种基金the Guangdong Basic and Applied Basic Research Foundation(Grant No.2022A1515011814)the China Meteorological Administration Tornado Key Laboratory(Grant No.TKL202302)the Science and Technology Research Project of Guangdong Meteorological Service(Grant No.GRMC2023Q35)。
文摘This study presents finely resolved radar signatures of multiple cyclonic vortices associated with an EF2 tornadic supercell that occurred in Guangzhou on 16 June 2022 and discusses how the mesocyclone formed on the lee side of mountain.A nearby X-band phased-array radar provides evidence that the mesocyclone was shallow,with a depth generally confined to less than 3 km.The mesocyclonic feature was observed to initiate from near-ground level,driven by the interaction between intensifying cold pool surges and shallow lee-side ambient flows.It was first recognized shortly after the presence of near-ground cyclonic convergence signatures over the leading edges of cold pool outflows.Over the subsequent 17 min,the mesocyclone developed upward,reaching a maximum height of 3 km,and produced a tornado 8min later.Nearly coinciding with the time of tornadogenesis,a noticeable separation of the low-level tornado cyclone from the midlevel mesocyclone was observed.This shift in the vertically oriented vortex tube was likely caused by modifications to the low-level flow due to the complex hilly terrain or by occlusions associated with rear-flank downdrafts.After tornadogenesis,high-resolution X-PAR observations revealed that the lowest-level mesocyclonic signature contracted into a gate-to-gate tornadic vortex signature(TVS)at the tip of hook echoes.Compared to conventional S-band operational weather radars,rapid-scan X-PAR observations indicate that a core diameter threshold of 1.5–2 km could be employed to identify a cyclonically sheared radial velocity couplet as a TVS,potentially extending the lead time for Doppler-based tornado warnings.
基金Natural Science Foundation of Fujian Province(2023J011338)Guided Foundation of Xiamen Science and Technology Bureau(3502Z20214ZD4009,3502Z20214ZD4010)+1 种基金Key Projects of East China Phased Array Weather Radar Application Joint Laboratory(EPJL_RP2025010)National Natural Science Foundation of China(41905049)。
文摘In September 2020,a pioneering observational network of three X-band phased-array radars(XPARs)was established in Xiamen,a subtropical coastal and densely populated city in southeastern China.Statistically,this study demonstrated that the XPAR network outperforms single S-band radar in revealing the warm-season convective storms in Xiamen in a fine-scale manner.The findings revealed that convective activity in Xiamen is most frequent in the central and northern mountainous regions,with lower frequency observed in the southern coastal areas.The diurnal pattern of convection occurrence exhibited a unimodal distribution,with a peak in the afternoon.The frequent occurrence of convective storms correlates well in both time and space with the active terrain uplift that occurs when the prevailing winds encounter mountainous areas.Notably,September stands apart with a bimodal diurnal pattern,featuring a prominent afternoon peak and a significant secondary peak before midnight.Further examination of dense rain gauge data in Xiamen indicates that high-frequency areas of short-duration heavy rainfall largely coincide with regions of active convective storms,except for a unique rainfall hotspot in southern Xiamen,where moderate convection frequency is accompanied by substantial rainfall.This anomalous rainfall,predominantly nocturnal,appears less influenced by terrain uplift and exhibits higher precipitation efficiency than daytime rainfall.These preliminary findings offer insights into the characteristics of convection occurrence in Xiamen's subtropical coastal environment and hold promise for enhancing the accuracy of convection and precipitation forecasts in similar environments.
基金National Key R&D Program of China(2022YFC3004101)Guangdong Basic and Applied Basic Research Foundation(2023A1515011971)+3 种基金Science and Tech-nology Projects in Guangzhou(2023B04J0232)Science and Technology Development Fund Project of Guangdong Meteor-ological Bureau(GRMC2022Q23,GRMC2022Q01)Jiangmen Basic and Applied Basic Research Key Programs(202312)Science and Technology Development Fund Project of Jiangmen Meteorological Bureau(202008,202004,201907,202007,201704)。
文摘To verify the detection capability of X-band dual-polarization phased-array radar for forest fires,this paper utilizes X-band dual-polarization phased-array radar data,Himawari-8 satellite data,combined with ground meteorological automatic station data.A case study of a forest fire in Ao Feng Mountain on February 19,2021,was conducted to comparatively analyze the monitoring results from these two remote sensing methods.The results show that both methods exhibit significant features associated with the forest fire process observed and are effective modern methods of forest fire monitoring.The Himawari-8 satellite identified the fire point at 07:10(LST;LST=UTC+8)with subsequent observations every 10 minutes until 10:00,nearly two hours before the fire was fully extinguished.Compared with the satellite,the Xband dual polarization phased array radar detectedthe fire 14 minutes earlier,with an improved temporal resolution of one minute,and was not affected by cloud cover.In the triggering stage,vigorous stage,sustained burning stage,and extinguishing stage of the forest fire,radar characteristic factors including reflectivity(Z),differential reflectivity(ZDR),and correlation coefficient(CC)showed strong correlations with the fire progression.The radar monitoring results were continuous,complete,and precise.In summary,the X-band dual-polarization phased-array radar offers more detailed detection information,shorter detection time interval,and higher detection spatial accuracy.It presents a promising new method for forest fire detection,providing crucial guidance for on-site rescue operations,particularly for small-scale fire events.
基金Key-Area R&D Program of Guangdong Province(2020B1111200001)National Key R&D Program of China(2017YFC1501701)+1 种基金National Natural Science Foundation of China(41875051)Guangzhou Municipal Science and Technology Planning Project(201903010101)
文摘The strong destructive winds during tornadoes can greatly threaten human life and destroy property.The increasing availability of visual and remote observations,especially by Doppler weather radars,is of great value in understanding tornado formation and issuing warnings to the public.In this study,we present the first documented tornado over water detected by a state-of-the-art dual-polarization phased-array radar(dual-PAR)in China.In contrast to new-generation weather radars,the dual-PAR shows great advantages in tornado detection for its high spatial resolution,reliable polarimetric variables,and rapid-scan strategy.The polarimetric signature of copolar cross-correlation coefficient with anomalously low magnitude appears to be effective for verifying a tornado and thus is helpful for issuing tornado warnings.The Guangdong Meteorological Service has been developing an experimental X-band dual-PAR network in the Pearl River Delta with the goal of deploying at least 40 advanced dual-PARs and other dual-polarization weather radars before 2035.This network is the first quasi-operational X-band dual-PAR network with unprecedented high coverage in the globe.With such high-performance close-range PARs,efficient operational nowcasting and warning services for small-scale,rapidly evolving,and damaging weather(e.g.,tornadoes,localized heavy rainfall,microbursts,and hail)can be expected.
基金funded by National High-Tech Research and Development Projects (863 Grant No. 2007AA061901)+2 种基金the National Key Program for Developing Basic Sciences (Grant No. 2012CB417202)the National Natural Science Foundation of China (Grant No. 41175038)the Public Welfare Meteorological Special Project (Grant No. GYHY201106046)
文摘An X-band phased-array meteorological radar （XPAR） was developed in China and will be installed in an airplane to observe precipitation systems for research purposes.In order to examine the observational capability of the XPAR and to test the operating mode and calibration before installation in the airplane,a mobile X-band Doppler radar （XDR） and XPAR were installed at the same site to observe convective precipitation events.Nearby S-band operational radar （SA） data were also collected to examine the reflectivity bias of XPAR.An algorithm for quantitative analysis of reflectivity and velocity differences and radar sensitivity of XPAR is presented.The reflectivity and velocity biases of XPAR are examined with SA and XDR.Reflectivity sensitivities,the horizontal and vertical structures of reflectivity by the three radars are compared and analyzed.The results indicated that while the XPRA with different operating modes can capture the main characteristic of 3D structures of precipitation,and the averaged reflectivity differences between XPAR and XDR,and XDR and SA,were 0.4 dB and 6.6 dB on 13 July and-4.5 dB and 5.1 dB on 2 August 2012,respectively.The minimum observed reflectivities at a range of 50 km for XPAR,XDR and SA were about 15.4 dBZ,13.5 dBZ and-3.5 dBZ,respectively.The bias of velocity between XPAR and XDR was negligible.This study provides a possible method for the quantitative comparison of the XPAR data,as well as the sensitivity of reflectivity,calibration,gain and bias introduced by pulse compression.
基金supported by the Pre-research Fund (N0901-041)the Funding of Jiangsu Innovation Program for Graduate Education(CX09B 081Z CX10B 110Z)
文摘A novel adaptive sampling interval algorithm for multitarget tracking is presented. This algorithm which is based on interacting multiple models incorporates the grey relational grade （GRG） into the particle swarm optimization （PSO）. Firstly, the desired tracking accuracy is set for each target. Secondly, sampling intervals are selected as particles, and then the advantage of the GRG is taken as the measurement function for resource management. Meanwhile, the fitness value of the PSO is used to measure the difference between desired tracking accuracy and estimated tracking accuracy. Finally, it is suggested that the radar should track the target whose prediction value of the next sampling interval is the smallest. Simulations show that the proposed method improves both the tracking accuracy and tracking efficiency of the phased-array radar.
基金Guangzhou Science and Technology Plan Project(202103000030)Guangdong Meteorological Bureau Science and Technology Project(GRMC2020Z08)a project co-funded by the Development Team of Radar Application and Severe Convection Early Warning Technology(GRMCTD202002)。
文摘The performance of different quantitative precipitation estimation(QPE) relationships is examined using the polarimetric variables from the X-band polarimetric phased-array radars in Guangzhou,China.Three QPE approaches,namely,R(ZH),R(ZH,ZDR) and R(KDP),are developed for horizontal reflectivity,differential reflectivity and specific phase shift rate,respectively.The estimation parameters are determined by fitting the relationships to the observed radar variables using the T-matrix method.The QPE relationships were examined using the data of four heavy precipitation events in southern China.The examination shows that the R(ZH) approach performs better for the precipitation rate less than 5 mm h-1, and R(KDP) is better for the rate higher than 5 mm h-1, while R(ZH,ZDR) has the worst performance.An adaptive approach is developed by taking the advantages of both R(ZH) and R(KDP) approaches to improve the QPE accuracy.
基金Supported by the Joint Fund Project of National Natural Science Foundation of China(U2142210).
文摘Dual-Doppler radar detection and wind-field retrieval techniques are crucial for capturing small-scale structures within convective systems.The spatiotemporal resolution of radar data is a key factor influencing the accuracy of wind-field observations.Recently,an advanced X-band phased-array weather radar system was deployed in Foshan,Guangdong Province,China,comprising a central collaborative control unit and multiple networked phased-array radar front-ends.These radar front-ends work together to scan a common area,achieving a maximum data time difference of 5 s and a volume scan interval of 30 s,thereby providing three-dimensional wind-field data with higher spatiotemporal resolution and greater accuracy than achieved using traditional methods.This study utilized the X-band phased-array weather radar system to analyze the development of a substantial hailstorm that occurred over Foshan on 26 March 2022.Analysis indicated that hail cloud activity intensified considerably after 1442 local time,with the maximum reflectivity factor exceeding 60 dBZ above the altitude of the-20℃ level,and reflectivity continued to increase over the subsequent 12 min.More precise information on the flow-field structure of the storm was obtained by examining the X-band radar data.The temporal and vertical variations in the maximum reflectivity factor,updraft velocity,vertical wind shear,and horizontal wind speed within a hailstorm cloud were scrutinized.The results show that the altitude,intensity,and range of the main updraft area increased as the storm core ascended.Concurrently,the vertical wind shear at mid-lower levels of the storm became more pronounced as the altitude of the strong radar echo center increased prior to the peak of the updraft.Therefore,a new hail warning index was developed by using the vertical wind shear,and the index can be used to issue warnings up to 12 min earlier than achievable using traditional methods detecting increases in hailstorm intensity.
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jointly supported by the National Natural Science Foundation of China(Grant U2442214)the China Meteorological Administration Youth Innovation Team(Grant No.CMA2024QN10)+1 种基金the National Defense Science and Technology Bureau’s 14th Five-Year Civil Aerospace Preresearch Project(Grant Nos.D030303 and D040204)the International Space Water Cycle Observation Constellation Program(Grant No.183311KYSB20200015).
文摘China launched its first spaceborne Precipitation Measurement Radar(PMR)on the FY-3G satellite in April 2023.To achieve the scientific goal of measuring the three-dimensional precipitation structure,evaluating the quantitative measurement ability of the PMR is critical.China operates more than 250 weather radars over the mainland.Consistency of the spaceborne radar with ground-based radars will enhance precipitation measurement ability,especially over oceans and mountains where observations are sparse.Additionally,the spaceborne radar can be used to evaluate the spatial and temporal homogeneity of the ground-based radar network.This paper focuses on comparing the PMR onboard the FY-3G satellite with S-band China New Generation Weather Radars(CINRADs).A comparison algorithm between the PMR and CINRADs has been developed,incorporating detailed quality control,attenuation correction,data optimization,spatiotemporal matching,non-uniform beam filling constraint,uniformity constraint,and frequency correction.The matched data in typical months of four seasons were selected to carry out the comparison.The data consistency between the PMR and CINRADs was analyzed.The correlation coefficient is 0.87,the deviation is 0.89 dB,and the standard deviation is 2.50 dB,based on 98226 matching samples.The results show the radar reflectivity of the PMR is quite comparable to that of the CINRADs,demonstrating that the PMR data quality is satisfactory and can be used to verify and correct data consistency among multiple ground-based radars.This work also paves the way for data fusion and joint application of satellite and ground radars in the future.
基金supported by the National Research Foundation(NRF)funded by the Korean government(MSIT)(Grant Nos.2022R1A2C1012361,2022R1A6A3A 13073165 and RS-2025-02242970).
文摘Dual-polarization(dual-pol)radar variables provide information about the quantity,type,size,and water content of hydrometeors.Assimilating these dual-pol radar variables into numerical weather prediction models can enhance forecast accuracy.Observation operators are essential for radar data assimilation.This study focuses on applying a realistic dual-pol radar observation operator to more accurately calculate dual-pol radar variables.Previously reported dual-pol radar observation operators tended to overestimate radar variables near 0℃ in convective precipitation and simulate unrealistic dual-pol radar variables in subfreezing regions.To address this,the improved operator(KNU dual-pol radar observation operator;K-DROP)limits the distribution of mixed-phase hydrometeors,which have both solid and liquid properties,in areas with strong updrafts and downdrafts,improving the overestimation of radar variables near the melting layer.Additionally,by applying the observed snow axis ratio during winter to K-DROP,the issue of differential reflectivity(Z_(DR))being calculated as a constant value in subfreezing regions has been improved.By incorporating the observed maximum radius of hydrometeors into K-DROP,the overestimation of reflectivity(Z_(H))in subfreezing regions,the overestimation of Z_(DR)in warm regions,and the underestimation of specific differential phase(K_(DP))in subfreezing regions and overestimation in warm regions,are improved.Compared to previous operators,the enhanced version reported in the present work produces more realistic dual-pol radar variables.
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 Innovation and Development Special Project of the China Meteorological Administration(Grant No.CXFZ2024J058)the Guangdong Province Basic and Applied Basic Research Foundation Meteorological Joint Fund Project(Grant No.2024A1515510036)+1 种基金the National Key R&D Program of China(Grant No.2022YFC3004101)the Technical Innovation Team Project of Guangzhou Meteorological Satellite Ground Station(Grant No.CXTD202401).
文摘FY-3G is the first polar-orbiting satellite equipped with a precipitation measurement radar(PMR)operating at Ku-andKa-band frequencies in China.In this study,we compare the reflectivity data from the FY-3G PMR Ku product and groundbasedradars(GRs)during 2024.Also,the FY-3G PMR is used as a third-party reference to evaluate the reflectivityconsistency among different GRs.The FY-3G PMR and GRs share similarities in their general distribution,characteristics,and intensity of reflectivity in strong precipitation cloud systems,though the former presents less detailed system structure.Systematic deviations between the FY-3G PMR and GRs and between GRs are comparable,albeit the reflectivity of the FY-3G PMR is generally slightly stronger than that of GRs(especially X-band GRs),with a mean bias ranging from 0.7 to 1.7dB.S-band GRs exhibit the smallest systematic deviation(STD=3.09 dB)from the FY-3G PMR,whereas the X-band GRsshow the largest(STD=3.61 dB),indirectly indicating the highest internal consistency among S-band GRs and the lowestamong X-band GRs.Besides,both S-and C-band GRs display similar deviations when paired with the FY-3G PMR as wellas when paired with their adjacent S/C-band GRs,suggesting good consistency between these two bands.In contrast,XbandGRs exhibit relatively poor consistency with S-band GRs and the FY-3G PMR,showing a deviation ranging from 3.0to 4.6 dB.
基金supported by the National Natural Science Foundation of China (NSFC) (Grant No.42205044)Feng Yun Application Pioneering Project (FY-APP) Innovation Center for Feng Yun Meteorological Satellite (FYSIC) Special Project (FY-APP-XC-2023.04)the Wuxi University Research Start-up Fund for Recruited Talent。
文摘Ground-based radar is the primary means by which severe storms are monitored and tracked;however, due to limited coverage, important data is often missed over ocean and mountainous areas. On the other hand, geostationary(GEO)weather satellites provide continuous observations with seamless coverage with advanced imager, despite their limited capability to penetrate clouds. Combining satellite and ground-radar observations could exploit the advantages of both techniques, providing tracking capability close to that of ground radar while maintaining full spatial coverage. This study presents a novel method called Multi-dimensional satellite Observation information for Radar Estimation(MORE) to reconstruct radar composite reflectivity(CREF). Deep learning techniques are important components of MORE for estimating CREF from China's Fengyun-4B(FY-4B) GEO satellite observations. Two models are developed: an infraredonly(IR-Single) model available for all times, and a visible-infrared(VIS+IR) model for daytime applications. These models incorporate multi-dimensional satellite observation information, including temporal, spatial, spectral, and viewing angle information, to enhance the accuracy of radar echo reconstruction. Results demonstrate that the VIS+IR model outperforms the IR-Single model, and both models achieves a root-mean-square error(RMSE) of less than 6 dBZ and a coefficient of determination(R~2) of greater than 0.7. The models effectively reconstruct radar echoes, including strong echoes exceeding 50 dBZ, and show good agreement with precipitation data in radar-blind areas. This study offers a valuable solution for severe weather monitoring and tracking in regions lacking ground-based radar observations, and provides a potential tool for enhanced data assimilation in numerical weather prediction(NWP) models.
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.
基金funded by Shandong Provincial Natural Science Foundation,grant number ZR2023MF111.
文摘To address the issues of poor adaptability in resource allocation and low multi-agent cooperation efficiency in Joint Radar and Communication(JRC)systems under dynamic environments,an intelligent optimization framework integrating Deep Reinforcement Learning(DRL)and Graph Neural Network(GNN)is proposed.This framework models resource allocation as a Partially Observable Markov Game(POMG),designs a weighted reward function to balance radar and communication efficiencies,adopts the Multi-Agent Proximal Policy Optimization(MAPPO)framework,and integrates Graph Convolutional Networks(GCN)and Graph Sample and Aggregate(Graph-SAGE)to optimize information interaction.Simulations show that,compared with traditional methods and pure DRL methods,the proposed framework achieves improvements in performance metrics such as communication success rate,Average Age of Information(AoI),and policy convergence speed,effectively enabling resource management in complex environments.Moreover,the proposed GNN-DRL-based intelligent optimization framework obtains significantly better performance for resource management in multi-agent JRC systems than traditional methods and pure DRL methods.
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National Natural Science Foundation of China under Grant 62571248 and Grant 62201266Key Laboratory of Intelligent Space TTC&O(Space Engineering University),Ministry of Education under Grant CYK2025-01-12。
文摘Integrated sensing and communication(ISAC)is an appealing approach to address spectrum congestion and beamforming is an effective method to realize ISAC.In this paper,we investigate the beamforming design problem for multiple-input multipleoutput(MIMO)ISAC systems and propose to maximize the radar beampattern gain of the target direction while ensuring the signal-to-interference-plus-noise ratio(SINR)constraints of communication users.Particularly,we discuss two cases of ISAC transmit beamforming,i.e.,Case-Ⅰand Case-Ⅱ,which do not have and do have the dedicated probing signal,respectively.For these two cases of transmit beamforming design problems,we start from the single-user scenario and provide the closed-form solutions for MIMO ISAC beamforming vectors.Then,we consider the multiuser scenario and utilize the semidefinite relaxation technique to convert the beamforming design problems into convex semidefinite programming problems.Furthermore,we investigate the impact of the channel correlation between radar and communication on the performance gain of MIMO ISAC systems and characterize the performance tradeoff.Numerical results validate that the dedicated probing signal is unnecessary in the single-user scenario,whereas it has a slight improvement in target detection performance at low SINR thresholds in the multi-user scenario.It is also shown that the stronger the correlation between radar and communication channels,the greater the performance gain of the system.
