This paper presents a quadcopter system for naviga-tion in outdoor urban environments.The main contributions include the hardware design,the establishment of global occu-pancy grid maps based on millimeter-wave radars...This paper presents a quadcopter system for naviga-tion in outdoor urban environments.The main contributions include the hardware design,the establishment of global occu-pancy grid maps based on millimeter-wave radars,the trajec-tory planning scheme based on optimal virtual tube methods,and the controller structure based on dynamics.The proposed system focuses on utilizing a compact and lightweight quadro-tor with sensors to achieve navigation that conforms to the direction of urban roads with high computational efficiency and safety.Our work is an application of millimeter-wave radars and virtual tube planning for obstacle avoidance in navigation.The validness and effectiveness of the proposed system are verified by experiments.展开更多
Millimeter-wave radar,with advantages such as non-contact penetration detection and privacy protection,has become a promising solution for unobtrusive monitoring in the field of smart elderly care.To solve the problem...Millimeter-wave radar,with advantages such as non-contact penetration detection and privacy protection,has become a promising solution for unobtrusive monitoring in the field of smart elderly care.To solve the problem of whether there are human body in the elderly care scene,this study proposed a method for judging the presence of a human body based on adaptive dual thresholds to reduce invalid vital sign detection in an empty environment.This method used a low-frequency energy ratio as the core judgment basis.It combined adaptive thresholds to accurately judge the presence of human targets,effectively reducing false detections caused by background interference.In addition,given the defect that variational mode extraction(VME)needs to rely on manual parameter adjustment based on empirical values,the crown porcupine optimization(CPO)algorithm is introduced to optimize the VME parameters adaptively,and the optimized VME is used to reconstruct the heartbeat signal to improve the signal purity.Then,the multiple signal classification(MUSIC)algorithm was used for spectrum analysis to improve the accuracy of heart rate estimation.The results show that in the experimental judgment of personnel,the miss rate in the case of personnel presence is 2.2%,and the false alarm rate in the case of no personnel is only 3.2%;the root mean square error and mean absolute error of the proposed heart rate(HR)estimation method are reduced by 4.4 beat per minute and 3.05 beat per minute respectively compared with the traditional VME,verifying its excellence.展开更多
In the application of the physical iterative method to retrieve millimeter-wave radar liquid water content(LWC)and liquid water path(LWP),particle parameter scheme is the main factor affecting retrieval performance.In...In the application of the physical iterative method to retrieve millimeter-wave radar liquid water content(LWC)and liquid water path(LWP),particle parameter scheme is the main factor affecting retrieval performance.In this paper,synchronous measurements of an airborne millimeter-wave radar and a hot-wire probe in stratus cloud are used to compare the LWC retrievals of the oceanic and continental particle parameter scheme with diameter less than 50μm and the particle parameter scheme with diameter less than 500μm and 1500μm(scheme 1,scheme 2,scheme 3,and scheme4,respectively).The results show that the particle parameter scheme needs to be selected according to the reflectivity factor when using the physical iterative method to retrieve the LWC and LWP.When the reflectivity factor is less than-30 d BZ,the retrieval error of scheme 1 is the minimum.When the reflectivity factor is greater than-30 d BZ,the retrieval error of scheme 4 is the minimum.Based on the reflectance factor value,the LWP retrievals of scheme 4 are closer to the measurements,the average relative bias is 5.2%,and the minimum relative bias is 4.4%.Compared with other schemes,scheme 4 seems to be more useful for the LWC and LWP retrieval of stratus cloud in China.展开更多
Gesture recognition plays an increasingly important role as the requirements of intelligent systems for human-computer interaction methods increase.To improve the accuracy of the millimeter-wave radar gesture detectio...Gesture recognition plays an increasingly important role as the requirements of intelligent systems for human-computer interaction methods increase.To improve the accuracy of the millimeter-wave radar gesture detection algorithm with limited computational resources,this study improves the detection performance in terms of optimized features and interference filtering.The accuracy of the algorithm is improved by refining the combination of gesture features using a self-constructed dataset,and biometric filtering is introduced to reduce the interference of inanimate object motion.Finally,experiments demonstrate the effectiveness of the proposed algorithm in both mitigating interference from inanimate objects and accurately recognizing gestures.Results show a notable 93.29%average reduction in false detections achieved through the integration of biometric filtering into the algorithm’s interpretation of target movements.Additionally,the algorithm adeptly identifies the six gestures with an average accuracy of 96.84%on embedded systems.展开更多
Millimeter-wave(mmWave)radar communication has emerged as an important technique for future wireless systems.However,the interference between the radar signal and communication data is the main issue that should be co...Millimeter-wave(mmWave)radar communication has emerged as an important technique for future wireless systems.However,the interference between the radar signal and communication data is the main issue that should be considered for the joint radar communication system.In this paper,a co-sharing waveform(CSW)is proposed to achieve communication and radar sensing simultaneously.To eliminate the co-interference between the communication and sensing signal,signal splitting and processing methods for communication data demodulation and radar signal processing are given respectively.Simulation results show that the bit error rate(BER)of CSW is close to that of the pure communication waveform.Moreover,the proposed CSW can achieve better performance than the existing waveforms in terms of range and velocity estimation.展开更多
Gestures are one of the most natural and intuitive approach for human-computer interaction.Compared with traditional camera-based or wearable sensors-based solutions,gesture recognition using the millimeter wave radar...Gestures are one of the most natural and intuitive approach for human-computer interaction.Compared with traditional camera-based or wearable sensors-based solutions,gesture recognition using the millimeter wave radar has attracted growing attention for its characteristics of contact-free,privacy-preserving and less environmentdependence.Although there have been many recent studies on hand gesture recognition,the existing hand gesture recognition methods still have recognition accuracy and generalization ability shortcomings in shortrange applications.In this paper,we present a hand gesture recognition method named multiscale feature fusion(MSFF)to accurately identify micro hand gestures.In MSFF,not only the overall action recognition of the palm but also the subtle movements of the fingers are taken into account.Specifically,we adopt hand gesture multiangle Doppler-time and gesture trajectory range-angle map multi-feature fusion to comprehensively extract hand gesture features and fuse high-level deep neural networks to make it pay more attention to subtle finger movements.We evaluate the proposed method using data collected from 10 users and our proposed solution achieves an average recognition accuracy of 99.7%.Extensive experiments on a public mmWave gesture dataset demonstrate the superior effectiveness of the proposed system.展开更多
Recently,there has been increased attention toward 3D imaging using single-pixel single-photon detection(also known as temporal data)due to its potential advantages in terms of cost and power efficiency.However,to eli...Recently,there has been increased attention toward 3D imaging using single-pixel single-photon detection(also known as temporal data)due to its potential advantages in terms of cost and power efficiency.However,to eliminate the symmetry blur in the reconstructed images,a fixed background is required.This paper proposes a fusion-data-based 3D imaging method that utilizes a single-pixel single-photon detector and millimeter-wave radar to capture temporal histograms of a scene from multiple perspectives.Subsequently,the 3D information can be reconstructed from the one-dimensional fusion temporal data by using an artificial neural network.Both the simulation and experimental results demonstrate that our fusion method effectively eliminates symmetry blur and improves the quality of the reconstructed images.展开更多
Converged communication and radar sensing systems have attained increasing attention in recent years.The development of converged radar-data systems is reviewed,with a special focus on millimeter/terahertz systems as ...Converged communication and radar sensing systems have attained increasing attention in recent years.The development of converged radar-data systems is reviewed,with a special focus on millimeter/terahertz systems as a promising trend.Firstly,we present historical development and convergence technology concept for communication-radar systems,and highlight some emerging technologies in this area.We then provide an updated and comprehensive survey of several converged systems operating in different microwave and millimeter frequency bands,by providing some selective typical communication and radar sensing systems.In this part,we also summarize and compare the system performance in terms of maximum range/range resolution for radar mode and Bit Error Rate(BER)/wireless distance for communication mode.In the last section,the convergence of millimeter/terahertz communication-radar system is concluded by analyzing the prospect of millimeter-wave/terahertz technologies in providing ultrafast data rates and high resolution for our smart future.展开更多
Millimeter-wave cloud radar(MMCR)provides the capability of detecting the features of micro particles inside clouds and describing the internal microphysical structure of the clouds.Therefore,MMCR has been widely appl...Millimeter-wave cloud radar(MMCR)provides the capability of detecting the features of micro particles inside clouds and describing the internal microphysical structure of the clouds.Therefore,MMCR has been widely applied in cloud observations.However,due to the influence of non-meteorological factors such as insects,the cloud observations are often contaminated by non-meteorological echoes in the clear air,known as clear-air echoes.It is of great significance to automatically identify the clear-air echoes in order to extract effective meteorological information from the complex weather background.The characteristics of clear-air echoes are studied here by combining data from four devices:an MMCR,a laser-ceilometer,an L-band radiosonde,and an all-sky camera.In addition,a new algorithm,which includes feature extraction,feature selection,and classification,is proposed to achieve the automatic identification of clear-air echoes.The results show that the recognition algorithm is fairly satisfied in both simple and complex weather conditions.The recognition accuracy can reach up to 95.86%for the simple cases when cloud echoes and clear-air echoes are separate,and 88.38%for the complicated cases when low cloud echoes and clear-air echoes are mixed.展开更多
As a basic property of cloud,accurate identification of cloud type is useful in forecasting the evolution of landfalling typhoons.Millimeter-wave cloud radar is an important means of identifying cloud type.Here,we dev...As a basic property of cloud,accurate identification of cloud type is useful in forecasting the evolution of landfalling typhoons.Millimeter-wave cloud radar is an important means of identifying cloud type.Here,we develop a fuzzy logic algorithm that depends on radar range-height-indicator(RHI)data and takes into account the fundamental physical features of different cloud types.The algorithm is applied to a ground-based Ka-band millimeter-wave cloud radar.The input parameters of the algorithm include average reflectivity factor intensity,ellipse long axis orientation,cloud base height,cloud thickness,presence/absence of precipitation,ratio of horizontal extent to vertical extent,maximum echo intensity,and standard variance of intensities.The identified cloud types are stratus(St),stratocumulus(Sc),cumulus(Cu),cumulonimbus(Cb),nimbostratus(Ns),altostratus(As),altocumulus(Ac)and high cloud.The cloud types identified using the algorithm are in good agreement with those identified by a human observer.As a case study,the algorithm was applied to typhoon Khanun(1720),which made landfall in south-eastern China in October 2017.Sequential identification results from the algorithm clearly reflected changes in cloud type and provided indicative information for forecasting of the typhoon.展开更多
A millimeter-wave linear frequency modulated continuous wave(LFM CW)radar is applied to water surface detection.This paper presents the experiment and imaging algorithm.In imaging processing,water surface texture can ...A millimeter-wave linear frequency modulated continuous wave(LFM CW)radar is applied to water surface detection.This paper presents the experiment and imaging algorithm.In imaging processing,water surface texture can hardly be seen in the results obtained by traditional imaging algorithm.To solve this problem,we propose a millimeter-wave LFMCW radar imaging algorithm for water surface texture.Different from the traditional imaging algorithm,the proposed imaging algorithm includes two improvements as follows:Firstly,the interference from static targets is removed through a frequency domainfilter;Secondly,the multiplicative noises are reduced by the maximum likelihood estimation method,which is used to estimatethe azimuth spectrum parameters to calculate the energy of water surface echo.Final results show that the proposed algorithmcan obtain water surface texture,which means that the proposed algorithm is superior to the traditional imaging algorithm.展开更多
This paper considers the non-line-of-sight(NLOS)vehicle localization problem by using millimeter-wave(MMW)automotive radar.Several preliminary attempts for NLOS vehicle detection are carried out and achieve good resul...This paper considers the non-line-of-sight(NLOS)vehicle localization problem by using millimeter-wave(MMW)automotive radar.Several preliminary attempts for NLOS vehicle detection are carried out and achieve good results.Firstly,an electromagnetic(EM)wave NLOS multipath propagation model for vehicle scene is established.Subsequently,with the help of available multipath echoes,a complete NLOS vehicle localiza-tion algorithm is proposed.Finally,simulation and experimental results validate the effectiveness of the established EM wave propagation model and the proposed NLOS vehicle localization algorithm.展开更多
This paper presented a real-time millimeter wave radar-based system for tracking vehicle trajectories in a wide area (continuously along a roadway with essentially no length limit in practice). The trajectory tracking...This paper presented a real-time millimeter wave radar-based system for tracking vehicle trajectories in a wide area (continuously along a roadway with essentially no length limit in practice). The trajectory tracking results were first validated for single vehicle trajectory tracking using the Real-Time Kinematic positioning technology based on the Beidou satellite navigation systems. The validation showed that the vehicle positions were captured with a mean lateral offset of −0.284 m and a mean longitudinal offset of −0.352 m. The mean estimated speeds were found to have a difference of only −0.048 km/h from the ground truths. The trajectory tracking results were also validated through multi-object tracking using video data from an unmanned aerial vehicle (UAV). Compared with the UAV video footage, the millimeter wave-based system was found to correctly capture around 92% of the total number of vehicles. For the correctly captured vehicles, their positions were found to be within 0.99 m (about a quarter of the width of a regular traffic lane) of the ground truth. In this paper, we also would like to share openly the entire validated datasets which has been recently published online as the TJRD TS platform. Additionally, a demonstration in using the dataset to detect aggressive driving behaviors, such as speeding was presented. It is expected that the open dataset will help enable researchers and practitioners to further explore the behaviors of road users, track the dynamics of safety risks and congestion formation, and evaluate incident impacts in a more microscopic but comprehensive way.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
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
基金supported by the National Key Research and Development Program of China(2022YFA1004703)the National Natural Science Foundation of China(62088101).
文摘This paper presents a quadcopter system for naviga-tion in outdoor urban environments.The main contributions include the hardware design,the establishment of global occu-pancy grid maps based on millimeter-wave radars,the trajec-tory planning scheme based on optimal virtual tube methods,and the controller structure based on dynamics.The proposed system focuses on utilizing a compact and lightweight quadro-tor with sensors to achieve navigation that conforms to the direction of urban roads with high computational efficiency and safety.Our work is an application of millimeter-wave radars and virtual tube planning for obstacle avoidance in navigation.The validness and effectiveness of the proposed system are verified by experiments.
基金supported by National Natural Science Foundation of China(Nos.62320106002 and U22A2014)National Key Research and Development Program of China(No.2021YFA1401103)2022 Wuxi Taihu Talent Program:Innovative Leading Talent Team(No.1096010241230120).
文摘Millimeter-wave radar,with advantages such as non-contact penetration detection and privacy protection,has become a promising solution for unobtrusive monitoring in the field of smart elderly care.To solve the problem of whether there are human body in the elderly care scene,this study proposed a method for judging the presence of a human body based on adaptive dual thresholds to reduce invalid vital sign detection in an empty environment.This method used a low-frequency energy ratio as the core judgment basis.It combined adaptive thresholds to accurately judge the presence of human targets,effectively reducing false detections caused by background interference.In addition,given the defect that variational mode extraction(VME)needs to rely on manual parameter adjustment based on empirical values,the crown porcupine optimization(CPO)algorithm is introduced to optimize the VME parameters adaptively,and the optimized VME is used to reconstruct the heartbeat signal to improve the signal purity.Then,the multiple signal classification(MUSIC)algorithm was used for spectrum analysis to improve the accuracy of heart rate estimation.The results show that in the experimental judgment of personnel,the miss rate in the case of personnel presence is 2.2%,and the false alarm rate in the case of no personnel is only 3.2%;the root mean square error and mean absolute error of the proposed heart rate(HR)estimation method are reduced by 4.4 beat per minute and 3.05 beat per minute respectively compared with the traditional VME,verifying its excellence.
基金National Natural Science Foundation of China(41575031,41175089)China Postdoctoral Science Foundation(2015M580124)Key Laboratory of Geo-Information Engineering(S18701)
文摘In the application of the physical iterative method to retrieve millimeter-wave radar liquid water content(LWC)and liquid water path(LWP),particle parameter scheme is the main factor affecting retrieval performance.In this paper,synchronous measurements of an airborne millimeter-wave radar and a hot-wire probe in stratus cloud are used to compare the LWC retrievals of the oceanic and continental particle parameter scheme with diameter less than 50μm and the particle parameter scheme with diameter less than 500μm and 1500μm(scheme 1,scheme 2,scheme 3,and scheme4,respectively).The results show that the particle parameter scheme needs to be selected according to the reflectivity factor when using the physical iterative method to retrieve the LWC and LWP.When the reflectivity factor is less than-30 d BZ,the retrieval error of scheme 1 is the minimum.When the reflectivity factor is greater than-30 d BZ,the retrieval error of scheme 4 is the minimum.Based on the reflectance factor value,the LWP retrievals of scheme 4 are closer to the measurements,the average relative bias is 5.2%,and the minimum relative bias is 4.4%.Compared with other schemes,scheme 4 seems to be more useful for the LWC and LWP retrieval of stratus cloud in China.
基金supported by the National Natural Science Foundation of China(No.12172076)。
文摘Gesture recognition plays an increasingly important role as the requirements of intelligent systems for human-computer interaction methods increase.To improve the accuracy of the millimeter-wave radar gesture detection algorithm with limited computational resources,this study improves the detection performance in terms of optimized features and interference filtering.The accuracy of the algorithm is improved by refining the combination of gesture features using a self-constructed dataset,and biometric filtering is introduced to reduce the interference of inanimate object motion.Finally,experiments demonstrate the effectiveness of the proposed algorithm in both mitigating interference from inanimate objects and accurately recognizing gestures.Results show a notable 93.29%average reduction in false detections achieved through the integration of biometric filtering into the algorithm’s interpretation of target movements.Additionally,the algorithm adeptly identifies the six gestures with an average accuracy of 96.84%on embedded systems.
基金supported by the National Natural Science Foundation of China(No.62171052 and No.61971054)the Fundamental Research Funds for the Central Universities(No.24820232023YQTD01).
文摘Millimeter-wave(mmWave)radar communication has emerged as an important technique for future wireless systems.However,the interference between the radar signal and communication data is the main issue that should be considered for the joint radar communication system.In this paper,a co-sharing waveform(CSW)is proposed to achieve communication and radar sensing simultaneously.To eliminate the co-interference between the communication and sensing signal,signal splitting and processing methods for communication data demodulation and radar signal processing are given respectively.Simulation results show that the bit error rate(BER)of CSW is close to that of the pure communication waveform.Moreover,the proposed CSW can achieve better performance than the existing waveforms in terms of range and velocity estimation.
基金supported by the National Natural Science Foundation of China under grant no.62272242.
文摘Gestures are one of the most natural and intuitive approach for human-computer interaction.Compared with traditional camera-based or wearable sensors-based solutions,gesture recognition using the millimeter wave radar has attracted growing attention for its characteristics of contact-free,privacy-preserving and less environmentdependence.Although there have been many recent studies on hand gesture recognition,the existing hand gesture recognition methods still have recognition accuracy and generalization ability shortcomings in shortrange applications.In this paper,we present a hand gesture recognition method named multiscale feature fusion(MSFF)to accurately identify micro hand gestures.In MSFF,not only the overall action recognition of the palm but also the subtle movements of the fingers are taken into account.Specifically,we adopt hand gesture multiangle Doppler-time and gesture trajectory range-angle map multi-feature fusion to comprehensively extract hand gesture features and fuse high-level deep neural networks to make it pay more attention to subtle finger movements.We evaluate the proposed method using data collected from 10 users and our proposed solution achieves an average recognition accuracy of 99.7%.Extensive experiments on a public mmWave gesture dataset demonstrate the superior effectiveness of the proposed system.
基金supported by the Shenzhen Science and Technology Program(Nos.JCYJ2022081 and 8102014029)the National Natural Science Foundation of China(No.62171458)the National Key Research and Development Program of China(No.2021YFB2802004)。
文摘Recently,there has been increased attention toward 3D imaging using single-pixel single-photon detection(also known as temporal data)due to its potential advantages in terms of cost and power efficiency.However,to eliminate the symmetry blur in the reconstructed images,a fixed background is required.This paper proposes a fusion-data-based 3D imaging method that utilizes a single-pixel single-photon detector and millimeter-wave radar to capture temporal histograms of a scene from multiple perspectives.Subsequently,the 3D information can be reconstructed from the one-dimensional fusion temporal data by using an artificial neural network.Both the simulation and experimental results demonstrate that our fusion method effectively eliminates symmetry blur and improves the quality of the reconstructed images.
基金supported in part by National Natural Science Foundation of China(NSFC)under Grant No.61771424in part by Natural Science Foundation of Zhejiang Province under Grant No.LZ18F010001.
文摘Converged communication and radar sensing systems have attained increasing attention in recent years.The development of converged radar-data systems is reviewed,with a special focus on millimeter/terahertz systems as a promising trend.Firstly,we present historical development and convergence technology concept for communication-radar systems,and highlight some emerging technologies in this area.We then provide an updated and comprehensive survey of several converged systems operating in different microwave and millimeter frequency bands,by providing some selective typical communication and radar sensing systems.In this part,we also summarize and compare the system performance in terms of maximum range/range resolution for radar mode and Bit Error Rate(BER)/wireless distance for communication mode.In the last section,the convergence of millimeter/terahertz communication-radar system is concluded by analyzing the prospect of millimeter-wave/terahertz technologies in providing ultrafast data rates and high resolution for our smart future.
基金supported by the National Key R&D Program of China(Grant No.2018YFC1506605)Sichuan Provincial Department of Education Scientific research projects(Grant No.16ZB0211)Chengdu University of Information Technology research and development projects(Grant No.CRF201705)。
文摘Millimeter-wave cloud radar(MMCR)provides the capability of detecting the features of micro particles inside clouds and describing the internal microphysical structure of the clouds.Therefore,MMCR has been widely applied in cloud observations.However,due to the influence of non-meteorological factors such as insects,the cloud observations are often contaminated by non-meteorological echoes in the clear air,known as clear-air echoes.It is of great significance to automatically identify the clear-air echoes in order to extract effective meteorological information from the complex weather background.The characteristics of clear-air echoes are studied here by combining data from four devices:an MMCR,a laser-ceilometer,an L-band radiosonde,and an all-sky camera.In addition,a new algorithm,which includes feature extraction,feature selection,and classification,is proposed to achieve the automatic identification of clear-air echoes.The results show that the recognition algorithm is fairly satisfied in both simple and complex weather conditions.The recognition accuracy can reach up to 95.86%for the simple cases when cloud echoes and clear-air echoes are separate,and 88.38%for the complicated cases when low cloud echoes and clear-air echoes are mixed.
基金This work was supported by the National Natural Science Foundation of China(Grant No.41675029)the National Basic Research Program of China(No.2013CB430102).
文摘As a basic property of cloud,accurate identification of cloud type is useful in forecasting the evolution of landfalling typhoons.Millimeter-wave cloud radar is an important means of identifying cloud type.Here,we develop a fuzzy logic algorithm that depends on radar range-height-indicator(RHI)data and takes into account the fundamental physical features of different cloud types.The algorithm is applied to a ground-based Ka-band millimeter-wave cloud radar.The input parameters of the algorithm include average reflectivity factor intensity,ellipse long axis orientation,cloud base height,cloud thickness,presence/absence of precipitation,ratio of horizontal extent to vertical extent,maximum echo intensity,and standard variance of intensities.The identified cloud types are stratus(St),stratocumulus(Sc),cumulus(Cu),cumulonimbus(Cb),nimbostratus(Ns),altostratus(As),altocumulus(Ac)and high cloud.The cloud types identified using the algorithm are in good agreement with those identified by a human observer.As a case study,the algorithm was applied to typhoon Khanun(1720),which made landfall in south-eastern China in October 2017.Sequential identification results from the algorithm clearly reflected changes in cloud type and provided indicative information for forecasting of the typhoon.
文摘A millimeter-wave linear frequency modulated continuous wave(LFM CW)radar is applied to water surface detection.This paper presents the experiment and imaging algorithm.In imaging processing,water surface texture can hardly be seen in the results obtained by traditional imaging algorithm.To solve this problem,we propose a millimeter-wave LFMCW radar imaging algorithm for water surface texture.Different from the traditional imaging algorithm,the proposed imaging algorithm includes two improvements as follows:Firstly,the interference from static targets is removed through a frequency domainfilter;Secondly,the multiplicative noises are reduced by the maximum likelihood estimation method,which is used to estimatethe azimuth spectrum parameters to calculate the energy of water surface echo.Final results show that the proposed algorithmcan obtain water surface texture,which means that the proposed algorithm is superior to the traditional imaging algorithm.
基金supported by the National Natural Science Foundation of China(62201510,62001091,61801435,61871080,61801435)the Initial Scientific Research Foundation of University of Science and Technology of China(Y030202059018051)+2 种基金Yangtze River Scholar Program,Sichuan Science and Technology Program(2019JDJQ0014)111 Project(B17008)Henan Provincial Department of Science and Technology Research Project(202102210315,212102210029,202102210-137).
文摘This paper considers the non-line-of-sight(NLOS)vehicle localization problem by using millimeter-wave(MMW)automotive radar.Several preliminary attempts for NLOS vehicle detection are carried out and achieve good results.Firstly,an electromagnetic(EM)wave NLOS multipath propagation model for vehicle scene is established.Subsequently,with the help of available multipath echoes,a complete NLOS vehicle localiza-tion algorithm is proposed.Finally,simulation and experimental results validate the effectiveness of the established EM wave propagation model and the proposed NLOS vehicle localization algorithm.
基金supported by the National Key R&D Program of China(2019YFB1600703)the Chinese National Natural Science Foundation(Grant No.72001161 and 52172348)the Fundamental Research Funds for the Central Universities.
文摘This paper presented a real-time millimeter wave radar-based system for tracking vehicle trajectories in a wide area (continuously along a roadway with essentially no length limit in practice). The trajectory tracking results were first validated for single vehicle trajectory tracking using the Real-Time Kinematic positioning technology based on the Beidou satellite navigation systems. The validation showed that the vehicle positions were captured with a mean lateral offset of −0.284 m and a mean longitudinal offset of −0.352 m. The mean estimated speeds were found to have a difference of only −0.048 km/h from the ground truths. The trajectory tracking results were also validated through multi-object tracking using video data from an unmanned aerial vehicle (UAV). Compared with the UAV video footage, the millimeter wave-based system was found to correctly capture around 92% of the total number of vehicles. For the correctly captured vehicles, their positions were found to be within 0.99 m (about a quarter of the width of a regular traffic lane) of the ground truth. In this paper, we also would like to share openly the entire validated datasets which has been recently published online as the TJRD TS platform. Additionally, a demonstration in using the dataset to detect aggressive driving behaviors, such as speeding was presented. It is expected that the open dataset will help enable researchers and practitioners to further explore the behaviors of road users, track the dynamics of safety risks and congestion formation, and evaluate incident impacts in a more microscopic but comprehensive way.
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 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.
