The meteor radar can detect the zenith angle,azimuth,radial velocity,and altitude of meteor trails so that one can invert the wind profiles in the mesosphere and low thermosphere(MLT)region,based on the Interferometri...The meteor radar can detect the zenith angle,azimuth,radial velocity,and altitude of meteor trails so that one can invert the wind profiles in the mesosphere and low thermosphere(MLT)region,based on the Interferometric and Doppler techniques.In this paper,the horizontal wind field,gravity wave(GW)disturbance variance,and GW fluxes are analyzed through the meteor radar observation from 2012−2022,at Mohe(53.5°N,122.4°E)and Zuoling(30.5°N,114.6°E)stations of the(Chinese)Meridian Project.The Lomb−Scargle periodogram method has been utilized to analyze the periodic variations for time series with observational data gaps.The results show that the zonal winds at both stations are eastward dominated,while the meridional winds are southward dominated.The variance of GW disturbances in the zonal and meridional directions increases gradually with height,and there is a strong pattern of annual variation.The zonal momentum flux of GW changes little with height,showing weak annual variation.The meridional GW flux varies gradually from northward to southward with height,and the annual periodicity is stronger.For both stations,the maximum values of zonal and meridional wind occur close to the peak heights of GW flux,with opposite directions.This observational evidence is consistent with the filtering theory.The horizontal wind velocity,GW flux,and disturbance variance of the GW at Mohe are overall smaller than those at Zuoling,indicating weaker activities in the MLT at Mohe.The power spectral density(PSD)calculated by the Lomb−Scargle periodogram shows that there are 12-month period and 6-month period in horizontal wind field,GW disturbance variance and GW flux at both stations,and especially there is also a 4-month cycle in the disturbance variance.The PSD of the 12-month and 6-month cycles exhibits maximum values below 88 km and above 94 km.展开更多
Accurate knowledge of mesospheric winds and waves is essential for studying the dynamics and climate in the mesosphere and lower thermosphere(MLT)region.In this study,we conduct a comparative analysis of the mesospher...Accurate knowledge of mesospheric winds and waves is essential for studying the dynamics and climate in the mesosphere and lower thermosphere(MLT)region.In this study,we conduct a comparative analysis of the mesosphere tidal results obtained from two adjacent meteor radars at low latitudes in Kunming,China,from November 2013 to December 2014.These two radars operate at different frequencies of 37.5 MHz and 53.1 MHz,respectively.However,overall good agreement is observed between the two radars in terms of horizontal winds and tide observations.The results show that the dominant tidal waves of the zonal and meridional winds are diurnal and semidiurnal tides.Moreover,we conduct an exhaustive statistical analysis to compare the tidal amplitudes and vertical wavelengths recorded by the dual radar systems,which reveals a high degree of alignment in tidal dynamics.The investigation includes variances and covariances of tidal amplitudes,which demonstrate remarkable consistency across measurements from both radars.This finding highlights clear uniformity in the mesospheric tidal patterns observed at low latitudes by the two neighboring meteor radars.Results of the comparative analysis specifically underscore the significant correlation in vertical wavelength measurements,validating the robustness of radar observations for tidal research.展开更多
Cognitive bias,stemming from electronic measurement error and variability in human perception,exists in cognitive electronic warfare and affects the outcomes of conflicts.In this paper,the dynamic game approach is emp...Cognitive bias,stemming from electronic measurement error and variability in human perception,exists in cognitive electronic warfare and affects the outcomes of conflicts.In this paper,the dynamic game approach is employed to develop a model for cognitive bias induced by incomplete information and measurement errors in cognitive radar countermeasures.The payoffs for both parties are calculated using the radar's anti-jamming strategy matrix A and the jammer's jamming strategy matrix B.With perfect Bayesian equilibrium,a dynamic radar countermeasure model is established,and the impact of cognitive bias is analyzed.Drawing inspiration from the cognitive bias analysis method used in stock market trading,a cognitive bias model for cognitive radar countermeasures is introduced,and its correctness is mathematically proved.A gaming scenario involving the AN/SPY-1 radar and a smart jammer is set up to analyze the influence of cognitive bias on game outcomes.Simulation results validate the effectiveness of the proposed method.展开更多
Automotive radar has emerged as a critical component in Advanced Driver Assistance Systems(ADAS)and autonomous driving,enabling robust environmental perception through precise range-Doppler and angular measurements.It...Automotive radar has emerged as a critical component in Advanced Driver Assistance Systems(ADAS)and autonomous driving,enabling robust environmental perception through precise range-Doppler and angular measurements.It plays a pivotal role in enhancing road safety by supporting accurate detection and localization of surrounding objects.However,real-world deployment of automotive radar faces significant challenges,including mutual interference among radar units and dense clutter due to multiple dynamic targets,which demand advanced signal processing solutions beyond conventional methodologies.This paper presents a comprehensive review of traditional signal processing techniques and recent advancements specifically designed to address contemporary operational challenges in automotive radar.Emphasis is placed on direction-of-arrival(DoA)estimation algorithms such as Bartlett beamforming,Minimum Variance Distortionless Response(MVDR),Multiple Signal Classification(MUSIC),and Estimation of Signal Parameters via Rotational Invariance Techniques(ESPRIT).Among these,ESPRIT offers superior resolution for multi-target scenarios with reduced computational complexity compared to MUSIC,making it particularly advantageous for real-time applications.Furthermore,the study evaluates state-of-the-art tracking algorithms,including the Kalman Filter(KF),Extended KF(EKF),Unscented KF,and Bayesian filter.EKF is especially suitable for radar systems due to its capability to linearize nonlinear measurement models.The integration of machine learning approaches for target detection and classification is also discussed,highlighting the trade-off between the simplicity of implementation in K-Nearest Neighbors(KNN)and the enhanced accuracy provided by Support Vector Machines(SVM).A brief overview of benchmark radar datasets,performance metrics,and relevant standards is included to support future research.The paper concludes by outlining ongoing challenges and identifying promising research directions in automotive radar signal processing,particularly in the context of increasingly complex traffic scenarios and autonomous navigation systems.展开更多
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
Existing through-wall human activity recognition methods often rely on Doppler information or reflective signal characteristics of the human body.However,static individuals,lacking prominent motion features,do not gen...Existing through-wall human activity recognition methods often rely on Doppler information or reflective signal characteristics of the human body.However,static individuals,lacking prominent motion features,do not generate Doppler information.Moreover,radar signals experience significant attenuation due to absorption and scattering effects as they penetrate walls,limiting recognition performance.To address these challenges,this study proposes a novel through-wall human activity recognition method based on MIMO radar.Utilizing a MIMO radar operating at 1–2 GHz,we capture activity data of individuals through walls and process it into range-angle maps to represent activity features.To tackle the issue of minimal variation in reflection areas caused by static individuals,a multi-scale activity feature extraction module is designed,capable of extracting effective features from radar signals across multiple scales.Simultaneously,a temporal attention mechanism is employed to extract keyframe information from sequential signals,focusing on critical moments of activity.Furthermore,this study introduces an activity recognition network based on a Deformable Transformer,which efficiently extracts both global and local features from radar signals,delivering precise human posture and activity sequences.In experimental scenarios involving 24 cm-thick brick walls,the proposed method achieves an impressive 97.1%accuracy in activity recognition classification.展开更多
The Global Precipitation Measurement(GPM)dual-frequency precipitation radar(DPR)products(Version 07A)are employed for a rigorous comparative analysis with ground-based operational weather radar(GR)networks.The reflect...The Global Precipitation Measurement(GPM)dual-frequency precipitation radar(DPR)products(Version 07A)are employed for a rigorous comparative analysis with ground-based operational weather radar(GR)networks.The reflectivity observed by GPM Ku PR is compared quantitatively against GR networks from CINRAD of China and NEXRAD of the United States,and the volume matching method is used for spatial matching.Additionally,a novel frequency correction method for all phases as well as precipitation types is used to correct the GPM Ku PR radar frequency to the GR frequency.A total of 20 GRs(including 10 from CINRAD and 10 from NEXRAD)are included in this comparative analysis.The results indicate that,compared with CINRAD matched data,NEXRAD exhibits larger biases in reflectivity when compared with the frequency-corrected Ku PR.The root-mean-square difference for CINRAD is calculated at 2.38 d B,whereas for NEXRAD it is 3.23 d B.The mean bias of CINRAD matched data is-0.16 d B,while the mean bias of NEXRAD is-2.10 d B.The mean standard deviation of bias for CINRAD is 2.15 d B,while for NEXRAD it is 2.29 d B.This study effectively assesses weather radar data in both the United States and China,which is crucial for improving the overall consistency of global precipitation estimates.展开更多
This paper proposes an integrated multi-stage framework to enhance frequency modulated continuous wave(FMCW)automotive radar performance under high noise and interference.The four-stage pipeline is applied consecutive...This paper proposes an integrated multi-stage framework to enhance frequency modulated continuous wave(FMCW)automotive radar performance under high noise and interference.The four-stage pipeline is applied consecutively:(i)an improved independent component analysis(ICA)blindly separates the two-channel echoes,isolating target and interference components;(ii)a recursive least-squares(RLS)filter compensates amplitude-and phase-mismatches,restoring signal fidelity;(iii)variational mode decomposition(VMD)followed by the Hilbert-Huang Transform(HHT)extracts noise-free intrinsic mode functions(IMFs)and sharpens their time-frequency signatures;and(iv)HHT-based beat-frequency estimation reconstructs a clean echo and delivers accurate range information.Finally,key IMFs are reconstructed into a clean signal,and a beat-frequency estimation via HHT confirms accurate distance results,closely aligning with theoretical predictions.On synthetic data with an input signal-to-noise ratio(SNR)of 12.7 dB,the pipeline delivers a 7.6 dB SNR gain,yields a mean-squared error of 0.25 m2,and achieves a range root-mean-square error(Range-RMSE)of 0.50 m.Empirical evaluations demonstrate that this enhanced ICA and VMD/HHT scheme effectively restores the fundamental echo signature,providing a robust approach for advanced driver assistance systems(ADAS).展开更多
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
The dwell scheduling problem for a multifunctional radar system is led to the formation of corresponding optimiza-tion problem.In order to solve the resulting optimization prob-lem,the dwell scheduling process in a sc...The dwell scheduling problem for a multifunctional radar system is led to the formation of corresponding optimiza-tion problem.In order to solve the resulting optimization prob-lem,the dwell scheduling process in a scheduling interval(SI)is formulated as a Markov decision process(MDP),where the state,action,and reward are specified for this dwell scheduling problem.Specially,the action is defined as scheduling the task on the left side,right side or in the middle of the radar idle time-line,which reduces the action space effectively and accelerates the convergence of the training.Through the above process,a model-free reinforcement learning framework is established.Then,an adaptive dwell scheduling method based on Q-learn-ing is proposed,where the converged Q value table after train-ing is utilized to instruct the scheduling process.Simulation results demonstrate that compared with existing dwell schedul-ing algorithms,the proposed one can achieve better scheduling performance considering the urgency criterion,the importance criterion and the desired execution time criterion comprehen-sively.The average running time shows the proposed algorithm has real-time performance.展开更多
Accurate estimation of rockfall trajectories is essential for mitigation of rockfall hazards.Nowadays,Doppler radar technologies can measure rockfall trajectories with centimeter resolution.Calibrating a numerical mod...Accurate estimation of rockfall trajectories is essential for mitigation of rockfall hazards.Nowadays,Doppler radar technologies can measure rockfall trajectories with centimeter resolution.Calibrating a numerical model to fit these measured trajectories,i.e.back analysis,often involves manual trial-anderror processes and subjective goodness-of-fit criteria.Here,we propose a framework that uses the chi-square statistic to quantify the misfit between modeled and measured rockfall trajectories.The framework can also quantify the uncertainty bounds on the best-fit model parameters.The approach is validated using field data from an Australian copper mine under two scenarios.(1)We perform an unconstrained back-analysis where the initial position and velocity of the rock,in addition to the coefficients of restitution(COR),are free variables.This scenario yields a normal COR Rn?0.866±0.109 and tangential COR R_(t)=0.29±0.151 with 68%confidence.(2)We perform a constrained back-analysis using predetermined initial position and velocity of the rock,which further constrains Rn to 0.8±0.014 and Rt to 0.39±0.065.Both scenarios show a higher uncertainty in Rt than in Rn.We also demonstrate the adaptability of the back-analysis framework to two-dimensional(2D)rockfall modeling using the same data.To the best of our knowledge,this is the first quantitative goodness-of-fit metric for trajectorybased rockfall back analysis that supports the estimation of inherent uncertainty.The simplicity of the metric lends itself to robust model optimization of rockfall back-analysis and can be adapted to other model assumptions(e.g.rigid-body mechanics)and metrics(e.g.velocity or energy).展开更多
Earth-based deep space radar studies celestial bodies by both transmitting and receiving radio waves,whereas radio telescopes only work passively.On the operational level,radar missions use only short observation time...Earth-based deep space radar studies celestial bodies by both transmitting and receiving radio waves,whereas radio telescopes only work passively.On the operational level,radar missions use only short observation times,which leaves a large portion of the time available for astronomical observations.However,the design principles used for radar and radio telescopes differ.Technical challenges are involved in making the instruments required to meet the requirements of these two applications simultaneously.In this study,we have attempted to tune a deep space radar system for use in radio astronomical applications and conducted a successful pulsar observation,thus demonstrating the feasibility of using radar systems,particularly distributed deep space radar,to perform astronomical research.Additionally,given the limited astronomical capacity available within the observed frequency range,this system has the potential to contribute to the long-term monitoring of specific radio sources.This work represents the first successful attempt to use an Earth-based deep space radar system to perform radio astronomy in China.We also discuss the challenges of tuning a built radar system for astronomical observation applications and propose recommendations for the design of future large-scale distributed deep space radar systems with innate astronomical capabilities.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
Numerical models play an important role in convective-scale forecasting,and dual-polarization radar observations can provide detailed microphysical data.In this study,we implement a direct assimilation operator for du...Numerical models play an important role in convective-scale forecasting,and dual-polarization radar observations can provide detailed microphysical data.In this study,we implement a direct assimilation operator for dual-polarization radar data using the hydrometeor background error covariance(HBEC)in the China Meteorological Administration MESO-scale weather forecasting system(CMA-MESO,formerly GRAPES-MESO)and conducted assimilation and forecasting experiments with X-band and S-band dual-polarization radar data on two cases.The results indicate that the direct assimilation of dual-polarization radar data enhanced the microphysical fields and the thermodynamic structure of convective systems to some extent based on the HBEC,thereby improving precipitation forecasts.Among the sensitivity tests of microphysical parameterization schemes,including the LIUMA scheme,the THOMPSON scheme,and the WSM6scheme(WRF Single-Moment 6-class),we find that the greatest improvement in the equivalent potential temperature,relative humidity,wind,and accumulated precipitation forecasts occurred in the experiment using the WSM6 scheme,as the distribution of solid precipitation particles was closer to the hydrometeor classification algorithm from the dualpolarization radar observations in our cases.展开更多
Accurate target angle estimation is one of the chal-lenges for wideband radars due to the fact that target occupies multiple range bins,resulting in lower energy or signal to noise ratio in a single range bin.This pap...Accurate target angle estimation is one of the chal-lenges for wideband radars due to the fact that target occupies multiple range bins,resulting in lower energy or signal to noise ratio in a single range bin.This paper proposes a processing technique for enhanced accuracy of target angle estimates for wideband monopulse radars.Firstly,to accumulate the energy of the received echo signals from different scatterers on a target,the phase difference between different scatterers on a target is estimated using the minimum entropy phase estimation method combining with the correlation between adjacent pulses.Then,the monopulse ratio is obtained by using the signals from the accumulated sum and difference channels.The target angle is estimated by weighting the accumulated echo energy for accu-racy enhancement.Experimental results based on both numeri-cal simulation and measured data are presented to validate the effectiveness of the proposed technique.展开更多
This study proposes a novel radar echo extrapolation algorithm,OF-ConvGRU,which integrates Optical Flow(OF)and Convolutional Gated Recurrent Unit(ConvGRU)methods for improved nowcasting.Using the Standardized Radar Da...This study proposes a novel radar echo extrapolation algorithm,OF-ConvGRU,which integrates Optical Flow(OF)and Convolutional Gated Recurrent Unit(ConvGRU)methods for improved nowcasting.Using the Standardized Radar Dataset of the Guangdong-Hong Kong-Macao Greater Bay Area,the performance of OF-ConvGRU was evaluated against OF and ConvGRU methods.Threat Score(TS)and Bias Score(BIAS)were employed to assess extrapolation accuracy across various echo intensities(20-50 dBz)and weather phenomena.Results demonstrate that OF-ConvGRU significantly enhances prediction accuracy for moderate-intensity echoes(30-40 dBz),effectively combining OF s precise motion estimation with ConvGRU s nonlinear learning capabilities.However,challenges persist in low-intensity(20 dBz)and high-intensity(50 dBz)echo predictions.The study reveals distinct advantages of each method in specific contexts,highlighting the importance of multi-method approaches in operational nowcasting.OF-ConvGRU shows promise in balancing short-term accuracy with long-term stability,particularly for complex weather systems.展开更多
Heart rate variability(HRV),as a key indicator for evaluating autonomic nervous system function,has significant value in areas such as cardiovascular disease screening and emotion monitoring.Although traditional conta...Heart rate variability(HRV),as a key indicator for evaluating autonomic nervous system function,has significant value in areas such as cardiovascular disease screening and emotion monitoring.Although traditional contact-based measurement methods offer high precision,they suffer from issues such as poor comfort and low user compliance.This paper proposes a non-contact HRV monitoring method using frequency modulated continuous wave(FMCW)radar,highlighting adaptive cycle segmentation and peak extraction as core innovations.Key advantages of this method include:1)effective suppression of motion artifacts and respiratory harmonics by leveraging cardiac energy concentration;2)precise heartbeat cycle identification across physiological states via adaptive segmentation,addressing time-varying differences;3)adaptive threshold adjustment using discrete energy signals and a support vector machine(SVM)model based on morphological-temporal-spectral characteristics,reducing complexity while maintaining precision.Previous approaches predominantly process radar signals holistically through algorithms to uniformly extract inter-beat intervals(IBIs),which may result in high computational complexity and inadequate dynamic adaptability.In contrast,our method achieved higher precision than conventional holistic processing approaches,while maintaining comparable precision with lower computational complexity than previous optimization algorithms.Experimental results demonstrate that the system achieves an average IBI error of 8.28 ms(RMSE of 15.3 ms),which is reduced by about 66%compared with the traditional holistically peak seeking method.The average errors of SDNN and RMSSD are 2.65 ms and 4.33 ms,respectively.More than 92%of the IBI errors are controlled within 20 ms.The distance adaptability test showed that although the accuracy of long-distance measurement decreased slightly(<6 ms),the overall detection performance remained robust at different distances.This study provided a novel estimation algorithm for non-contact HRV detection,offering new perspectives for future health monitoring.展开更多
In order to further enhance the numerical application of weather radar radial velocity,this paper proposes a quality control scheme for weather radar radial velocity from the perspective of data assimilation.The propo...In order to further enhance the numerical application of weather radar radial velocity,this paper proposes a quality control scheme for weather radar radial velocity from the perspective of data assimilation.The proposed scheme is based on the WRFDA(Weather Research and Forecasting Data Assimilation)system and utilizes the biweight algorithm to perform quality control on weather radar radial velocity data.A series of quality control tests conducted over the course of one month demonstrate that the scheme can be seamlessly integrated into the data assimilation process.The scheme is characterized by its simplicity,fast implementation,and ease of maintenance.By determining an appropri-ate threshold for quality control,the percentage of outliers identified by the scheme remains highly stable over time.Moreover,the mean errors and standard deviations of the O-B(observation-minus-background)values are significantly reduced,improving the overall data quality.The main information and spatial distribution features of the data are pre-served effectively.After quality control,the distribution of the O-B Probability Density Function is adjusted in a manner that brings it closer to a Gaussian distribution.This adjustment is beneficial for the subsequent data assimilation process,contributing to more accurate numerical weather predictions.Thus,the proposed quality control scheme provides a valuable tool for improving weather radar data quality and enhancing numerical forecasting performance.展开更多
Automatically recognizing radar emitters from com-plex electromagnetic environments is important but non-trivial.Moreover,the changing electromagnetic environment results in inconsistent signal distribution in the rea...Automatically recognizing radar emitters from com-plex electromagnetic environments is important but non-trivial.Moreover,the changing electromagnetic environment results in inconsistent signal distribution in the real world,which makes the existing approaches perform poorly for recognition tasks in different scenes.In this paper,we propose a domain generaliza-tion framework is proposed to improve the adaptability of radar emitter signal recognition in changing environments.Specifically,we propose an end-to-end denoising based domain-invariant radar emitter recognition network(DDIRNet)consisting of a denoising model and a domain invariant representation learning model(IRLM),which mutually benefit from each other.For the signal denoising model,a loss function is proposed to match the feature of the radar signals and guarantee the effectiveness of the model.For the domain invariant representation learning model,contrastive learning is introduced to learn the cross-domain feature by aligning the source and unseen domain distri-bution.Moreover,we design a data augmentation method that improves the diversity of signal data for training.Extensive experiments on classification have shown that DDIRNet achieves up to 6.4%improvement compared with the state-of-the-art radar emitter recognition methods.The proposed method pro-vides a promising direction to solve the radar emitter signal recognition problem.展开更多
基金supported by the Fundamental Research Funds for the Central Universities,CHD(NO.300102263205 and NO.300102264916)the West Light Cross-Disciplinary Innovation team of Chinese Academy of Sciences(NO.E1294301).supported by the Fundamental Research Funds for the Central Universities,CHD(NO.300102263205 and NO.300102264916)the West Light Cross-Disciplinary Innovation team of Chinese Academy of Sciences(NO.E1294301).
文摘The meteor radar can detect the zenith angle,azimuth,radial velocity,and altitude of meteor trails so that one can invert the wind profiles in the mesosphere and low thermosphere(MLT)region,based on the Interferometric and Doppler techniques.In this paper,the horizontal wind field,gravity wave(GW)disturbance variance,and GW fluxes are analyzed through the meteor radar observation from 2012−2022,at Mohe(53.5°N,122.4°E)and Zuoling(30.5°N,114.6°E)stations of the(Chinese)Meridian Project.The Lomb−Scargle periodogram method has been utilized to analyze the periodic variations for time series with observational data gaps.The results show that the zonal winds at both stations are eastward dominated,while the meridional winds are southward dominated.The variance of GW disturbances in the zonal and meridional directions increases gradually with height,and there is a strong pattern of annual variation.The zonal momentum flux of GW changes little with height,showing weak annual variation.The meridional GW flux varies gradually from northward to southward with height,and the annual periodicity is stronger.For both stations,the maximum values of zonal and meridional wind occur close to the peak heights of GW flux,with opposite directions.This observational evidence is consistent with the filtering theory.The horizontal wind velocity,GW flux,and disturbance variance of the GW at Mohe are overall smaller than those at Zuoling,indicating weaker activities in the MLT at Mohe.The power spectral density(PSD)calculated by the Lomb−Scargle periodogram shows that there are 12-month period and 6-month period in horizontal wind field,GW disturbance variance and GW flux at both stations,and especially there is also a 4-month cycle in the disturbance variance.The PSD of the 12-month and 6-month cycles exhibits maximum values below 88 km and above 94 km.
基金supported by the National Natural Science Foundation of China (Grant Nos. 42125402 and 42174183)the National Key Technologies R&D Program of China (Grant No.2022YFF0503703)+2 种基金the B-type Strategic Priority Program of the Chinese Academy of Sciences (Grant No. XDB41000000)the foundation of the National Key Laboratory of Electromagnetic Environment and the Fundamental Research Funds for the Central Universitiesthe Chinese Meridian Project
文摘Accurate knowledge of mesospheric winds and waves is essential for studying the dynamics and climate in the mesosphere and lower thermosphere(MLT)region.In this study,we conduct a comparative analysis of the mesosphere tidal results obtained from two adjacent meteor radars at low latitudes in Kunming,China,from November 2013 to December 2014.These two radars operate at different frequencies of 37.5 MHz and 53.1 MHz,respectively.However,overall good agreement is observed between the two radars in terms of horizontal winds and tide observations.The results show that the dominant tidal waves of the zonal and meridional winds are diurnal and semidiurnal tides.Moreover,we conduct an exhaustive statistical analysis to compare the tidal amplitudes and vertical wavelengths recorded by the dual radar systems,which reveals a high degree of alignment in tidal dynamics.The investigation includes variances and covariances of tidal amplitudes,which demonstrate remarkable consistency across measurements from both radars.This finding highlights clear uniformity in the mesospheric tidal patterns observed at low latitudes by the two neighboring meteor radars.Results of the comparative analysis specifically underscore the significant correlation in vertical wavelength measurements,validating the robustness of radar observations for tidal research.
文摘Cognitive bias,stemming from electronic measurement error and variability in human perception,exists in cognitive electronic warfare and affects the outcomes of conflicts.In this paper,the dynamic game approach is employed to develop a model for cognitive bias induced by incomplete information and measurement errors in cognitive radar countermeasures.The payoffs for both parties are calculated using the radar's anti-jamming strategy matrix A and the jammer's jamming strategy matrix B.With perfect Bayesian equilibrium,a dynamic radar countermeasure model is established,and the impact of cognitive bias is analyzed.Drawing inspiration from the cognitive bias analysis method used in stock market trading,a cognitive bias model for cognitive radar countermeasures is introduced,and its correctness is mathematically proved.A gaming scenario involving the AN/SPY-1 radar and a smart jammer is set up to analyze the influence of cognitive bias on game outcomes.Simulation results validate the effectiveness of the proposed method.
基金supported in part by the National Science and Technology Council,Taiwan:NSTC 113-2410-H-030-077-MY2.
文摘Automotive radar has emerged as a critical component in Advanced Driver Assistance Systems(ADAS)and autonomous driving,enabling robust environmental perception through precise range-Doppler and angular measurements.It plays a pivotal role in enhancing road safety by supporting accurate detection and localization of surrounding objects.However,real-world deployment of automotive radar faces significant challenges,including mutual interference among radar units and dense clutter due to multiple dynamic targets,which demand advanced signal processing solutions beyond conventional methodologies.This paper presents a comprehensive review of traditional signal processing techniques and recent advancements specifically designed to address contemporary operational challenges in automotive radar.Emphasis is placed on direction-of-arrival(DoA)estimation algorithms such as Bartlett beamforming,Minimum Variance Distortionless Response(MVDR),Multiple Signal Classification(MUSIC),and Estimation of Signal Parameters via Rotational Invariance Techniques(ESPRIT).Among these,ESPRIT offers superior resolution for multi-target scenarios with reduced computational complexity compared to MUSIC,making it particularly advantageous for real-time applications.Furthermore,the study evaluates state-of-the-art tracking algorithms,including the Kalman Filter(KF),Extended KF(EKF),Unscented KF,and Bayesian filter.EKF is especially suitable for radar systems due to its capability to linearize nonlinear measurement models.The integration of machine learning approaches for target detection and classification is also discussed,highlighting the trade-off between the simplicity of implementation in K-Nearest Neighbors(KNN)and the enhanced accuracy provided by Support Vector Machines(SVM).A brief overview of benchmark radar datasets,performance metrics,and relevant standards is included to support future research.The paper concludes by outlining ongoing challenges and identifying promising research directions in automotive radar signal processing,particularly in the context of increasingly complex traffic scenarios and autonomous navigation systems.
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supported by National Natural Science Foundation of China(No.62272242)Postgraduate Research&Practice Innovation Program of Jiangsu Province(Nos.KYCX21_0800,KYCX23_1082).
文摘Existing through-wall human activity recognition methods often rely on Doppler information or reflective signal characteristics of the human body.However,static individuals,lacking prominent motion features,do not generate Doppler information.Moreover,radar signals experience significant attenuation due to absorption and scattering effects as they penetrate walls,limiting recognition performance.To address these challenges,this study proposes a novel through-wall human activity recognition method based on MIMO radar.Utilizing a MIMO radar operating at 1–2 GHz,we capture activity data of individuals through walls and process it into range-angle maps to represent activity features.To tackle the issue of minimal variation in reflection areas caused by static individuals,a multi-scale activity feature extraction module is designed,capable of extracting effective features from radar signals across multiple scales.Simultaneously,a temporal attention mechanism is employed to extract keyframe information from sequential signals,focusing on critical moments of activity.Furthermore,this study introduces an activity recognition network based on a Deformable Transformer,which efficiently extracts both global and local features from radar signals,delivering precise human posture and activity sequences.In experimental scenarios involving 24 cm-thick brick walls,the proposed method achieves an impressive 97.1%accuracy in activity recognition classification.
基金funded by the National Key Research and Development Program of China(Grant No.2023YFB3907500)the National Natural Science Foundation(Grant No.42330602)the“Fengyun Satellite Remote Sensing Product Validation and Verification”Youth Innovation Team of the China Meteorological Administration(Grant No.CMA2023QN12)。
文摘The Global Precipitation Measurement(GPM)dual-frequency precipitation radar(DPR)products(Version 07A)are employed for a rigorous comparative analysis with ground-based operational weather radar(GR)networks.The reflectivity observed by GPM Ku PR is compared quantitatively against GR networks from CINRAD of China and NEXRAD of the United States,and the volume matching method is used for spatial matching.Additionally,a novel frequency correction method for all phases as well as precipitation types is used to correct the GPM Ku PR radar frequency to the GR frequency.A total of 20 GRs(including 10 from CINRAD and 10 from NEXRAD)are included in this comparative analysis.The results indicate that,compared with CINRAD matched data,NEXRAD exhibits larger biases in reflectivity when compared with the frequency-corrected Ku PR.The root-mean-square difference for CINRAD is calculated at 2.38 d B,whereas for NEXRAD it is 3.23 d B.The mean bias of CINRAD matched data is-0.16 d B,while the mean bias of NEXRAD is-2.10 d B.The mean standard deviation of bias for CINRAD is 2.15 d B,while for NEXRAD it is 2.29 d B.This study effectively assesses weather radar data in both the United States and China,which is crucial for improving the overall consistency of global precipitation estimates.
基金the National Science and Technology Council,Taiwan,for financially supporting this research(grant No.NSTC 113-2221-E-018-011)the Ministry of Education's Teaching Practice Research Program,Taiwan(PSK1134099).
文摘This paper proposes an integrated multi-stage framework to enhance frequency modulated continuous wave(FMCW)automotive radar performance under high noise and interference.The four-stage pipeline is applied consecutively:(i)an improved independent component analysis(ICA)blindly separates the two-channel echoes,isolating target and interference components;(ii)a recursive least-squares(RLS)filter compensates amplitude-and phase-mismatches,restoring signal fidelity;(iii)variational mode decomposition(VMD)followed by the Hilbert-Huang Transform(HHT)extracts noise-free intrinsic mode functions(IMFs)and sharpens their time-frequency signatures;and(iv)HHT-based beat-frequency estimation reconstructs a clean echo and delivers accurate range information.Finally,key IMFs are reconstructed into a clean signal,and a beat-frequency estimation via HHT confirms accurate distance results,closely aligning with theoretical predictions.On synthetic data with an input signal-to-noise ratio(SNR)of 12.7 dB,the pipeline delivers a 7.6 dB SNR gain,yields a mean-squared error of 0.25 m2,and achieves a range root-mean-square error(Range-RMSE)of 0.50 m.Empirical evaluations demonstrate that this enhanced ICA and VMD/HHT scheme effectively restores the fundamental echo signature,providing a robust approach for advanced driver assistance systems(ADAS).
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supported by the National Natural Science Foundation of China(6177109562031007).
文摘The dwell scheduling problem for a multifunctional radar system is led to the formation of corresponding optimiza-tion problem.In order to solve the resulting optimization prob-lem,the dwell scheduling process in a scheduling interval(SI)is formulated as a Markov decision process(MDP),where the state,action,and reward are specified for this dwell scheduling problem.Specially,the action is defined as scheduling the task on the left side,right side or in the middle of the radar idle time-line,which reduces the action space effectively and accelerates the convergence of the training.Through the above process,a model-free reinforcement learning framework is established.Then,an adaptive dwell scheduling method based on Q-learn-ing is proposed,where the converged Q value table after train-ing is utilized to instruct the scheduling process.Simulation results demonstrate that compared with existing dwell schedul-ing algorithms,the proposed one can achieve better scheduling performance considering the urgency criterion,the importance criterion and the desired execution time criterion comprehen-sively.The average running time shows the proposed algorithm has real-time performance.
基金funding from NSERC Alliance Grant ALLRP 576858e22 in partnership with Rocscience Inc.
文摘Accurate estimation of rockfall trajectories is essential for mitigation of rockfall hazards.Nowadays,Doppler radar technologies can measure rockfall trajectories with centimeter resolution.Calibrating a numerical model to fit these measured trajectories,i.e.back analysis,often involves manual trial-anderror processes and subjective goodness-of-fit criteria.Here,we propose a framework that uses the chi-square statistic to quantify the misfit between modeled and measured rockfall trajectories.The framework can also quantify the uncertainty bounds on the best-fit model parameters.The approach is validated using field data from an Australian copper mine under two scenarios.(1)We perform an unconstrained back-analysis where the initial position and velocity of the rock,in addition to the coefficients of restitution(COR),are free variables.This scenario yields a normal COR Rn?0.866±0.109 and tangential COR R_(t)=0.29±0.151 with 68%confidence.(2)We perform a constrained back-analysis using predetermined initial position and velocity of the rock,which further constrains Rn to 0.8±0.014 and Rt to 0.39±0.065.Both scenarios show a higher uncertainty in Rt than in Rn.We also demonstrate the adaptability of the back-analysis framework to two-dimensional(2D)rockfall modeling using the same data.To the best of our knowledge,this is the first quantitative goodness-of-fit metric for trajectorybased rockfall back analysis that supports the estimation of inherent uncertainty.The simplicity of the metric lends itself to robust model optimization of rockfall back-analysis and can be adapted to other model assumptions(e.g.rigid-body mechanics)and metrics(e.g.velocity or energy).
基金supported by the China Postdoctoral Science Foundation(2024M754113)the Chongqing Postdoctoral Innovative Fund(CQBX202419)the Natural Science Foundation of Chongqing(CSTB2023NSCOMSX0629).
文摘Earth-based deep space radar studies celestial bodies by both transmitting and receiving radio waves,whereas radio telescopes only work passively.On the operational level,radar missions use only short observation times,which leaves a large portion of the time available for astronomical observations.However,the design principles used for radar and radio telescopes differ.Technical challenges are involved in making the instruments required to meet the requirements of these two applications simultaneously.In this study,we have attempted to tune a deep space radar system for use in radio astronomical applications and conducted a successful pulsar observation,thus demonstrating the feasibility of using radar systems,particularly distributed deep space radar,to perform astronomical research.Additionally,given the limited astronomical capacity available within the observed frequency range,this system has the potential to contribute to the long-term monitoring of specific radio sources.This work represents the first successful attempt to use an Earth-based deep space radar system to perform radio astronomy in China.We also discuss the challenges of tuning a built radar system for astronomical observation applications and propose recommendations for the design of future large-scale distributed deep space radar systems with innate astronomical capabilities.
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sponsored by the National Natural Science Foundation of China(U2442601 and U2442218)the High Performance Computing Platform of Nanjing University of Information Science&Technology(NUIST)for their support of this work。
文摘Numerical models play an important role in convective-scale forecasting,and dual-polarization radar observations can provide detailed microphysical data.In this study,we implement a direct assimilation operator for dual-polarization radar data using the hydrometeor background error covariance(HBEC)in the China Meteorological Administration MESO-scale weather forecasting system(CMA-MESO,formerly GRAPES-MESO)and conducted assimilation and forecasting experiments with X-band and S-band dual-polarization radar data on two cases.The results indicate that the direct assimilation of dual-polarization radar data enhanced the microphysical fields and the thermodynamic structure of convective systems to some extent based on the HBEC,thereby improving precipitation forecasts.Among the sensitivity tests of microphysical parameterization schemes,including the LIUMA scheme,the THOMPSON scheme,and the WSM6scheme(WRF Single-Moment 6-class),we find that the greatest improvement in the equivalent potential temperature,relative humidity,wind,and accumulated precipitation forecasts occurred in the experiment using the WSM6 scheme,as the distribution of solid precipitation particles was closer to the hydrometeor classification algorithm from the dualpolarization radar observations in our cases.
文摘Accurate target angle estimation is one of the chal-lenges for wideband radars due to the fact that target occupies multiple range bins,resulting in lower energy or signal to noise ratio in a single range bin.This paper proposes a processing technique for enhanced accuracy of target angle estimates for wideband monopulse radars.Firstly,to accumulate the energy of the received echo signals from different scatterers on a target,the phase difference between different scatterers on a target is estimated using the minimum entropy phase estimation method combining with the correlation between adjacent pulses.Then,the monopulse ratio is obtained by using the signals from the accumulated sum and difference channels.The target angle is estimated by weighting the accumulated echo energy for accu-racy enhancement.Experimental results based on both numeri-cal simulation and measured data are presented to validate the effectiveness of the proposed technique.
基金Scientific Research and Development Project of Hebei Meteorological Bureau(23ky08).
文摘This study proposes a novel radar echo extrapolation algorithm,OF-ConvGRU,which integrates Optical Flow(OF)and Convolutional Gated Recurrent Unit(ConvGRU)methods for improved nowcasting.Using the Standardized Radar Dataset of the Guangdong-Hong Kong-Macao Greater Bay Area,the performance of OF-ConvGRU was evaluated against OF and ConvGRU methods.Threat Score(TS)and Bias Score(BIAS)were employed to assess extrapolation accuracy across various echo intensities(20-50 dBz)and weather phenomena.Results demonstrate that OF-ConvGRU significantly enhances prediction accuracy for moderate-intensity echoes(30-40 dBz),effectively combining OF s precise motion estimation with ConvGRU s nonlinear learning capabilities.However,challenges persist in low-intensity(20 dBz)and high-intensity(50 dBz)echo predictions.The study reveals distinct advantages of each method in specific contexts,highlighting the importance of multi-method approaches in operational nowcasting.OF-ConvGRU shows promise in balancing short-term accuracy with long-term stability,particularly for complex weather systems.
基金supported by National Natural Science Foundation of China(Nos.62320106002,U22A2014)National Key Research and Development Program of China(No.2021YFA1401103)+2 种基金2022 Wuxi Taihu Talent Program:Innovative Leading Talent Team(No.1096010241230120)Fundamental Research Funds for Central Universities(No.1322050205250910)Wuxi Municipal Basic Research Project(No.K20241026).
文摘Heart rate variability(HRV),as a key indicator for evaluating autonomic nervous system function,has significant value in areas such as cardiovascular disease screening and emotion monitoring.Although traditional contact-based measurement methods offer high precision,they suffer from issues such as poor comfort and low user compliance.This paper proposes a non-contact HRV monitoring method using frequency modulated continuous wave(FMCW)radar,highlighting adaptive cycle segmentation and peak extraction as core innovations.Key advantages of this method include:1)effective suppression of motion artifacts and respiratory harmonics by leveraging cardiac energy concentration;2)precise heartbeat cycle identification across physiological states via adaptive segmentation,addressing time-varying differences;3)adaptive threshold adjustment using discrete energy signals and a support vector machine(SVM)model based on morphological-temporal-spectral characteristics,reducing complexity while maintaining precision.Previous approaches predominantly process radar signals holistically through algorithms to uniformly extract inter-beat intervals(IBIs),which may result in high computational complexity and inadequate dynamic adaptability.In contrast,our method achieved higher precision than conventional holistic processing approaches,while maintaining comparable precision with lower computational complexity than previous optimization algorithms.Experimental results demonstrate that the system achieves an average IBI error of 8.28 ms(RMSE of 15.3 ms),which is reduced by about 66%compared with the traditional holistically peak seeking method.The average errors of SDNN and RMSSD are 2.65 ms and 4.33 ms,respectively.More than 92%of the IBI errors are controlled within 20 ms.The distance adaptability test showed that although the accuracy of long-distance measurement decreased slightly(<6 ms),the overall detection performance remained robust at different distances.This study provided a novel estimation algorithm for non-contact HRV detection,offering new perspectives for future health monitoring.
基金funded by Beijige Fund of Nanjing Joint Institute for Atmospheric Sciences(BJG202501)the Joint Research Project for Meteorological Capacity Improvement(22NLTSY009)+2 种基金Key Scientific Research Projects of Jiangsu Provincial Meteorological Bureau(KZ202203)China Meteorological Administration projects(CMAJBGS202316)the Guiding Research Projects of Jiangsu Provincial Meteorological Bureau(ZD202404,ZD202419).
文摘In order to further enhance the numerical application of weather radar radial velocity,this paper proposes a quality control scheme for weather radar radial velocity from the perspective of data assimilation.The proposed scheme is based on the WRFDA(Weather Research and Forecasting Data Assimilation)system and utilizes the biweight algorithm to perform quality control on weather radar radial velocity data.A series of quality control tests conducted over the course of one month demonstrate that the scheme can be seamlessly integrated into the data assimilation process.The scheme is characterized by its simplicity,fast implementation,and ease of maintenance.By determining an appropri-ate threshold for quality control,the percentage of outliers identified by the scheme remains highly stable over time.Moreover,the mean errors and standard deviations of the O-B(observation-minus-background)values are significantly reduced,improving the overall data quality.The main information and spatial distribution features of the data are pre-served effectively.After quality control,the distribution of the O-B Probability Density Function is adjusted in a manner that brings it closer to a Gaussian distribution.This adjustment is beneficial for the subsequent data assimilation process,contributing to more accurate numerical weather predictions.Thus,the proposed quality control scheme provides a valuable tool for improving weather radar data quality and enhancing numerical forecasting performance.
基金supported by the National Natural Science Foundation of China(62101575)the Research Project of NUDT(ZK22-57)the Self-directed Project of State Key Laboratory of High Performance Computing(202101-16).
文摘Automatically recognizing radar emitters from com-plex electromagnetic environments is important but non-trivial.Moreover,the changing electromagnetic environment results in inconsistent signal distribution in the real world,which makes the existing approaches perform poorly for recognition tasks in different scenes.In this paper,we propose a domain generaliza-tion framework is proposed to improve the adaptability of radar emitter signal recognition in changing environments.Specifically,we propose an end-to-end denoising based domain-invariant radar emitter recognition network(DDIRNet)consisting of a denoising model and a domain invariant representation learning model(IRLM),which mutually benefit from each other.For the signal denoising model,a loss function is proposed to match the feature of the radar signals and guarantee the effectiveness of the model.For the domain invariant representation learning model,contrastive learning is introduced to learn the cross-domain feature by aligning the source and unseen domain distri-bution.Moreover,we design a data augmentation method that improves the diversity of signal data for training.Extensive experiments on classification have shown that DDIRNet achieves up to 6.4%improvement compared with the state-of-the-art radar emitter recognition methods.The proposed method pro-vides a promising direction to solve the radar emitter signal recognition problem.
