Characterizing foliar trait variation in sun and shade leaves can provide insights into inter-and intra-species resource use strategies and plant response to environmental change.However,datasets with records of multi...Characterizing foliar trait variation in sun and shade leaves can provide insights into inter-and intra-species resource use strategies and plant response to environmental change.However,datasets with records of multiple foliar traits from the same individual and including shade leaves are sparse,which limits our ability to investigate trait-trait,trait-environment relationships and trait coordination in both sun and shade leaves.We presented a comprehensive dataset of 15 foliar traits from sun and shade leaves sampled with leaf spectroscopy,including 424 individuals of 110 plant species from 19 sites across eastern North America.We investigated trait variation,covariation,scaling relationships with leaf mass,and the effects of environment,canopy position,and taxonomy on trait expression.Generally,sun leaves had higher leaf mass per area,nonstructural carbohydrates and total phenolics,lower mass-based chlorophyll a+b,carotenoids,phosphorus,and potassium,but exhibited species-specific characteristics.Covariation between sun and shade leaf traits,and trait-environment relationships were overall consistent across species.The main dimensions of foliar trait variation in seed plants were revealed including leaf economics traits,photosynthetic pigments,defense,and structural traits.Taxonomy and canopy position collectively explained most of the foliar trait variation.This study highlights the importance of including intra-individual and intra-specific trait variation to improve our understanding of ecosystem functions.Our findings have implications for efficient field sampling,and trait mapping with remote sensing.展开更多
Ionomic profiles are primarily influenced by genetic and environmental factors.Identifying ionomic responses to varietal effects is necessary to understand the ionomic variations among species or subspecies and to pot...Ionomic profiles are primarily influenced by genetic and environmental factors.Identifying ionomic responses to varietal effects is necessary to understand the ionomic variations among species or subspecies and to potentially understand genetic effects on ionomic profiles.We cultivated 120 rice(Oryza sativa)varieties to seedling stage in identical hydroponic conditions and determined the concentrations of 26 elements(including 3 anions)in the shoots and roots of rice.Although the subspecies effects were limited by the genus Oryza pre-framework and its elemental chemical properties,we found significant differences in ionomic variations in most elements among the aus,indica and japonica subspecies.Principal component analysis of the correlations indicated that variations in the root-to-shoot ionomic transport mechanisms were the main causes of ionomic differences among the subspecies.Furthermore,the correlations were primarily associated with the screening of varieties for elemental covariation effects that can facilitate breeding biofortified rice varieties with safe concentrations of otherwise toxic elements.The japonica subspecies exhibited the strongest elemental correlations and elemental covariation effects,therefore,they showed greater advantages for biofortification than the indica and aus subspecies,whereas indica and aus subspecies were likely safer in metal(loid)polluted soils.We also found that geographical and historical distribution significantly defined the ionomic profiles.Overall,the results of this study provided a reference for further association studies to improve the nutritional status and minimize toxicity risks in rice production.展开更多
By employing the singular value decomposition(SVD) analysis, we have investigated in the present paper the covariations between circulation changes in the Northern(NH) and Southern Hemispheres(SH) and their associatio...By employing the singular value decomposition(SVD) analysis, we have investigated in the present paper the covariations between circulation changes in the Northern(NH) and Southern Hemispheres(SH) and their associations with ENSO by using the NCEP/NCAR reanalysis, the reconstructed monthly NOAA SST, and CMAP precipitation along with NOAA Climate Prediction Center(CPC) ENSO indices. A bi-hemispheric covariation mode(hereafter BHCM) is explored, which is well represented by the first mode of the SVD analysis of sea surface pressure anomaly(SLPA-SVD1). This SVD mode can explain 57.36% of the total covariance of SLPA. BHCM varies in time with a long-term trend and periodicities of 3—5 years. The long term trend revealed by SVD1 shows that the SLP increases in the equatorial central and eastern Pacific but decreases in the western Pacific and tropical Indian Ocean, which facilitates easterlies in the lower troposphere to be intensified and El Ni觡o events to occur with lower frequency. The spatial pattern of the BHCM looks roughly symmetric about the equator in the tropics, whereas it is characterized by zonal disturbances in the mid-latitude of NH and is highly associated with AAO in the mid-latitude of SH. On inter-annual time scales, the BHCM is highly correlated with ENSO. The atmosphere in both the NH and SH responds to sea surface temperature anomalies in the equatorial region, while the contemporaneous circulation changes in the NH and SH in turn affect the occurrence of El Ni觡o/La Ni觡a. In boreal winter, significant temperature and precipitation anomalies associated with the BHCM are found worldwide. Specifically, in the positive phase of the BHCM,temperature and precipitation are anomalously low in eastern China and some other regions of East Asia. These results are helpful for us to better understand interactions between circulations in the NH and SH and the dynamical mechanisms behind these interactions.展开更多
A previous study, focused on the correlation of muta-tion pairs of synonymous (S) and asynonymous (A) mutations, distinguished only between the treated and untreated data of protease and reverse tran-scriptase (RT) of...A previous study, focused on the correlation of muta-tion pairs of synonymous (S) and asynonymous (A) mutations, distinguished only between the treated and untreated data of protease and reverse tran-scriptase (RT) of HIV-1 subtype B. It is well known that single mutation patterns in HIV-1 are treat-ment-specific. It logically follows that covariation between mutations will also be treatment specific. Thus, our motivation is to give a more in depth study of the covariation between mutation pairs, analyzing not only treated and untreated, but what specific treatments were used, and how they affected the co-variation between the mutations differently. We in-tended to further deepen this study by analyzing the covariation of mutations in protease and RT in dif-ferent subtypes of HIV-1. We found that virus sam-ples subjected to antiretroviral Protease- and RT- inhibitors do show different patterns of mutation covariation in B-subtype protease and RT of HIV-1, while maintaining the same overall trend. covariation will tend to be higher and more distinct from and covariation after treatment. The same trend continues in protease and RT re-gardless of subtype. We also found the highly cova-ried codon positions, position pairs, and position- covariation clusters in protease, affected by different treatments. Most of them are well known major drug-resistance sites for these treatments.展开更多
We introduce the notion of symmetric covariation,which is a new measure of dependence between two components of a symmetricα-stable random vector,where the stability parameterαmeasures the heavy-tailedness of its di...We introduce the notion of symmetric covariation,which is a new measure of dependence between two components of a symmetricα-stable random vector,where the stability parameterαmeasures the heavy-tailedness of its distribution.Unlike covariation that exists only whenα∈(1,2],symmetric covariation is well defined for allα∈(0,2].We show that symmetric covariation can be defined using the proposed generalized fractional derivative,which has broader usages than those involved in this work.Several properties of symmetric covariation have been derived.These are either similar to or more general than those of the covariance functions in the Gaussian case.The main contribution of this framework is the representation of the characteristic function of bivariate symmetricα-stable distribution via convergent series based on a sequence of symmetric covariations.This series representation extends the one of bivariate Gaussian.展开更多
In underwater target search path planning,the accuracy of sonar models directly dictates the accurate assessment of search coverage.In contrast to physics-informed sonar models,traditional geometric sonar models fail ...In underwater target search path planning,the accuracy of sonar models directly dictates the accurate assessment of search coverage.In contrast to physics-informed sonar models,traditional geometric sonar models fail to accurately characterize the complex influence of marine environments.To overcome these challenges,we propose an acoustic physics-informed intelligent path planning framework for underwater target search,integrating three core modules:The acoustic-physical modeling module adopts 3D ray-tracing theory and the active sonar equation to construct a physics-driven sonar detection model,explicitly accounting for environmental factors that influence sonar performance across heterogeneous spaces.The hybrid parallel computing module adopts a message passing interface(MPI)/open multi-processing(Open MP)hybrid strategy for large-scale acoustic simulations,combining computational domain decomposition and physics-intensive task acceleration.The search path optimization module adopts the covariance matrix adaptation evolution algorithm to solve continuous optimization problems of heading angles,which ensures maximum search coverage for targets.Largescale experiments conducted in the Pacific and Atlantic Oceans demonstrate the framework's effectiveness:(1)Precise capture of sonar detection range variations from 5.45 km to 50 km in heterogeneous marine environments.(2)Significant speedup of 453.43×for acoustic physics modeling through hybrid parallelization.(3)Notable improvements of 7.23%in detection coverage and 15.86%reduction in optimization time compared to the optimal baseline method.The framework provides a robust solution for underwater search missions in complex marine environments.展开更多
Land surface temperature(LST)is the key variable in land-atmosphere interaction,having an important impact on weather and climate forecasting.However,achieving consistent analysis of LST and the atmosphere in assimila...Land surface temperature(LST)is the key variable in land-atmosphere interaction,having an important impact on weather and climate forecasting.However,achieving consistent analysis of LST and the atmosphere in assimilation is quite challenging.This is because there is limited knowledge about the cross-component background error covariance(BEC)between LST and atmospheric state variables.This study aims to clarify whether there is a relationship between the error of LST and atmospheric variables,and whether this relationship varies spatially and temporally.To this end,the BEC coupled with atmospheric variables and LST was constructed(LST-BEC),and its characteristics were analyzed based on the 2023 mei-yu season.The general characteristics of LST-BEC show that the LST is mainly correlated with the atmospheric temperature and the correlation decreases gradually with a rise in atmospheric height,and the error standard deviation of the LST is noticeably larger than that of the low-level atmospheric temperature.The spatiotemporal characteristics of LST-BEC on the heavy-rain day and light-rain day show that the error correlation and error standard deviation of LST and low-level atmospheric temperature and humidity are closely related to the weather background,and also have obvious diurnal variations.These results provide valuable information for strongly coupled land-atmosphere assimilation.展开更多
In this paper, the problem of cubature Kalman fusion filtering(CKFF) is addressed for multi-sensor systems under amplify-and-forward(AaF) relays. For the purpose of facilitating data transmission, AaF relays are utili...In this paper, the problem of cubature Kalman fusion filtering(CKFF) is addressed for multi-sensor systems under amplify-and-forward(AaF) relays. For the purpose of facilitating data transmission, AaF relays are utilized to regulate signal communication between sensors and filters. Here, the randomly varying channel parameters are represented by a set of stochastic variables whose occurring probabilities are permitted to exhibit bounded uncertainty. Employing the spherical-radial cubature principle, a local filter under AaF relays is initially constructed. This construction ensures and minimizes an upper bound of the filtering error covariance by designing an appropriate filter gain. Subsequently, the local filters are fused through the application of the covariance intersection fusion rule. Furthermore, the uniform boundedness of the filtering error covariance's upper bound is investigated through establishing certain sufficient conditions. The effectiveness of the proposed CKFF scheme is ultimately validated via a simulation experiment concentrating on a three-phase induction machine.展开更多
The simultaneous description for nuclear matter and finite nuclei has been a long-standing challenge in nuclear ab initio theory.With the success for nuclear matter,the relativistic Brueckner-Hartree-Fock(RBHF)theory ...The simultaneous description for nuclear matter and finite nuclei has been a long-standing challenge in nuclear ab initio theory.With the success for nuclear matter,the relativistic Brueckner-Hartree-Fock(RBHF)theory with covariant chiral interactions is a promising ab initio approach to describe both nuclear matter and finite nuclei.In the description of finite nuclei with the current RBHF theory,the covariant chiral interactions have to be localized to make calculations feasible.In order to examine the reliability and validity,in this letter,the RBHF theory with local and nonlocal covariant chiral interactions at leading order is applied to nuclear matter.The low-energy constants in the covariant chiral interactions determined with the local regularization are close to those with the nonlocal regularization.Moreover,the RBHF theory using covariant chiral interactions with local and nonlocal regulators provides an equally good description of the saturation properties of nuclear matter.The present work paves the way for the implementation of covariant chiral interactions in RBHF theory for finite nuclei.展开更多
本文主要对Hessian度量诱导的截面曲率展开分析。首先阐述Hessian度量和与之相关的Christoffor符号和曲率张量公式。接着介绍当定义域U为锥时,齐次函数的相关概念,以及Clebsch covariant S(f)和截面曲率的联系。利用R3中开子集U和超平面...本文主要对Hessian度量诱导的截面曲率展开分析。首先阐述Hessian度量和与之相关的Christoffor符号和曲率张量公式。接着介绍当定义域U为锥时,齐次函数的相关概念,以及Clebsch covariant S(f)和截面曲率的联系。利用R3中开子集U和超平面M = {f = 1}相切2-平面上一点处的截面曲率可由S(f)和Hessian行列式H(f)表示,其中f为R3上的齐次多项式,得到不变量S(f)为零与Witten-Dijkgraaf-Verlinde-Verlinde (WDVV)方程等价的条件,即f可以表示为两种特殊的形式。This paper primarily analyzes the sectional curvature induced by the Hessian metric. It begins by detailing the Hessian metric and its associated Christoffel symbols and curvature tensor formulas. It then introduces the concept of homogeneous functions when the domain U is a cone, as well as the relationship between the Clebsch covariant S(f) and the sectional curvature. Using an open subset U in R3 and a hypersurface M = {f = 1}, the sectional curvature at a point on a 2-plane tangent to M can be expressed in terms of S(f) and the Hessian matrix H(f), where f is a homogeneous polynomial on R3. The condition for the invariant S(f) to be zero is equivalent to the Witten-Dijkgraaf-Verlinde-Verlinde (WDVV) equations, which implies that f can be represented in two specific forms.展开更多
Surveillance systems can take various forms,but gait-based surveillance is emerging as a powerful approach due to its ability to identify individuals without requiring their cooperation.In the existing studies,several...Surveillance systems can take various forms,but gait-based surveillance is emerging as a powerful approach due to its ability to identify individuals without requiring their cooperation.In the existing studies,several approaches have been suggested for gait recognition;nevertheless,the performance of existing systems is often degraded in real-world conditions due to covariate factors such as occlusions,clothing changes,walking speed,and varying camera viewpoints.Furthermore,most existing research focuses on single-person gait recognition;however,counting,tracking,detecting,and recognizing individuals in dual-subject settings with occlusions remains a challenging task.Therefore,this research proposed a variant of an automated gait model for occluded dual-subject walk scenarios.More precisely,in the proposed method,we have designed a deep learning(DL)-based dual-subject gait model(DSG)involving three modules.The first module handles silhouette segmentation,localization,and counting(SLC)using Mask-RCNN with MobileNetV2.The next stage uses a Convolutional block attention module(CBAM)-based Siamese network for frame-level tracking with a modified gallery setting.Following the last,gait recognition based on regionbased deep learning is proposed for dual-subject gait recognition.The proposed method,tested on Shri Mata Vaishno Devi University(SMVDU)-Multi-Gait and Single-Gait datasets,shows strong performance with 94.00%segmentation,58.36%tracking,and 63.04%gait recognition accuracy in dual-subject walk scenarios.展开更多
As data becomes increasingly complex,measuring dependence among variables is of great interest.However,most existing measures of dependence are limited to the Euclidean setting and cannot effectively characterize the ...As data becomes increasingly complex,measuring dependence among variables is of great interest.However,most existing measures of dependence are limited to the Euclidean setting and cannot effectively characterize the complex relationships.In this paper,we propose a novel method for constructing independence tests for random elements in Hilbert spaces,which includes functional data as a special case.Our approach is using distance covariance of random projections to build a test statistic that is computationally efficient and exhibits strong power performance.We prove the equivalence between testing for independence expressed on the original and the projected covariates,bridging the gap between measures of testing independence in Euclidean spaces and Hilbert spaces.Implementation of the test involves calibration by permutation and combining several p-values from different projections using the false discovery rate method.Simulation studies and real data examples illustrate the finite sample properties of the proposed method under a variety of scenarios.展开更多
Satellite communication plays an important role in 6G systems.However,satellite communication systems are more susceptible to intentional or unintentional interference signals than other communication systems because ...Satellite communication plays an important role in 6G systems.However,satellite communication systems are more susceptible to intentional or unintentional interference signals than other communication systems because of their working mechanism of transparent forwarding.For the purpose of eliminating the influence of interference,this paper develops an angle reciprocal interference suppression scheme based on the reconstruction of interferenceplus-noise covariance matrix(ARIS-RIN).Firstly,we utilize the reciprocity between the known beam central angle and the unknown signal arrival angle to estimate the angle of arrival(AOA)of desired signal due to the multi-beam coverage.Then,according to the priori known spatial spectrum distribution,the interferenceplus-noise covariance matrix(INCM)is reconstructed by integrating within the range except the direction of desired signal.In order to correct the estimation bias of the first two steps,the worst-case performance optimization technology is adopted in the process of solving the beamforming vector.Numerical simulation results show that the developed scheme:1)has a higher output signal-to-interference-plus-noise ratio(SINR)under arbitrary signal-to-noise ratio(SNR);2)still has good performance under small snapshots;3)is robuster and easier to be realized when comparing with minimum variance distortionless response(MVDR)and the traditional diagonal loading algorithms.展开更多
The reuse of liquid propellant rocket engines has increased the difficulty of their control and estimation.State and parameter Moving Horizon Estimation(MHE)is an optimization-based strategy that provides the necessar...The reuse of liquid propellant rocket engines has increased the difficulty of their control and estimation.State and parameter Moving Horizon Estimation(MHE)is an optimization-based strategy that provides the necessary information for model predictive control.Despite the many advantages of MHE,long computation time has limited its applications for system-level models of liquid propellant rocket engines.To address this issue,we propose an asynchronous MHE method called advanced-multi-step MHE with Noise Covariance Estimation(amsMHE-NCE).This method computes the MHE problem asynchronously to obtain the states and parameters and can be applied to multi-threaded computations.In the background,the state and covariance estimation optimization problems are computed using multiple sampling times.In real-time,sensitivity is used to quickly approximate state and parameter estimates.A covariance estimation method is developed using sensitivity to avoid redundant MHE problem calculations in case of sensor degradation during engine reuse.The amsMHE-NCE is validated through three cases based on the space shuttle main engine system-level model,and we demonstrate that it can provide more accurate real-time estimates of states and parameters compared to other commonly used estimation methods.展开更多
The Mekong Delta in Vietnam is a region that produces rice and emits methane,a potent greenhouse gas.Vietnam’s rice exports,which rank among the top four globally,have a significant impact on the world’s food suppy....The Mekong Delta in Vietnam is a region that produces rice and emits methane,a potent greenhouse gas.Vietnam’s rice exports,which rank among the top four globally,have a significant impact on the world’s food suppy.The Eddy Covariance system,positioned in the rice field,has been recording methane emission rates and bio-meteorological factors.This study presents the findings of three crop seasons(Summer-Autumn 2020(S-A20),Winter-Spring 2021(W-S21),and Spring-Summer 2021(S-S21))from the year 2020 to 2021.The highest CH4 emission value was observed in the S-S21 crop,reaching 4.14μmol s^(-1 )m^(-2).Elevated CH_(4) emission rates were predominantly recorded during the vegetative stage within first 21 days after planting,while lower CH_(4) emissions were observed during the reproductive and ripening stages.This pattern clearly indicates higher methane emissions at the vegetative stage of the growing rice,likely due to the abundance of organic matter in the rice fields.The average CH4 emission rate was 0.1μmol m^(-2) s^(-1).Notably,high methane emissions were recorded when the soil surface temperature was below 33℃.As a results,the S-S21 exhibits the highest methane emission rates compared to other seasons.展开更多
In complex environments such as high dynamics and weak signals,a satellite signal compensation method based on prefabricated trajectory assistance and an improved adaptive Kalman filter is proposed for a 155 mm differ...In complex environments such as high dynamics and weak signals,a satellite signal compensation method based on prefabricated trajectory assistance and an improved adaptive Kalman filter is proposed for a 155 mm differential rotating rear-body control-guided projectile to address the situation of satellite signal flickering and loss in projectile navigation systems due to environmental limitations.First,establish the system state and measurement equation when receiving satellite signals normally.Second,a seven-degree-of-freedom external ballistic model is constructed,and the ideal trajectory output from the ballistic model is used to provide the virtual motion state of the projectile,which is input into a filter as a substitute observation when satellite signals are lost.Finally,an adaptive Kalman filter(AKF)is designed,the proposed adaptive Kalman filter can accurately adjust the estimation error covariance matrix and Kalman gain in real-time based on information covariance mismatch.The simulation results show that compared to the classical Kalman filter,it can reduce the average positioning error by more than 38.21%in the case of short-term and full-range loss of satellite signals,providing a new idea for the integrated navigation of projectiles with incomplete information under the condition of satellite signal loss.展开更多
Full waveform inversion methods evaluate the properties of subsurface media by minimizing the misfit between synthetic and observed data.However,these methods omit measurement errors and physical assumptions in modeli...Full waveform inversion methods evaluate the properties of subsurface media by minimizing the misfit between synthetic and observed data.However,these methods omit measurement errors and physical assumptions in modeling,resulting in several problems in practical applications.In particular,full waveform inversion methods are very sensitive to erroneous observations(outliers)that violate the Gauss–Markov theorem.Herein,we propose a method for addressing spurious observations or outliers.Specifically,we remove outliers by inverting the synthetic data using the local convexity of the Gaussian distribution.To achieve this,we apply a waveform-like noise model based on a specific covariance matrix definition.Finally,we build an inversion problem based on the updated data,which is consistent with the wavefield reconstruction inversion method.Overall,we report an alternative optimization inversion problem for data containing outliers.The proposed method is robust because it uses uncertainties.This method enables accurate inversion,even when based on noisy models or a wrong wavelet.展开更多
Urban areas are the major anthropogenic source of atmospheric CO_(2),thus making longterm and continuous observations of their carbon emission dynamics extremely important.The COVID-19 lockdown served as a natural exp...Urban areas are the major anthropogenic source of atmospheric CO_(2),thus making longterm and continuous observations of their carbon emission dynamics extremely important.The COVID-19 lockdown served as a natural experiment that provided a unique opportunity to analyse the contribution of human activities to CO_(2) emissions from urban areas.In 2020,Beijing experienced COVID-19 confinement with different levels of restrictions on social mobility and economic activity,resulting in reductions in CO_(2) emissions.To investigate the response mechanisms of CO_(2) flux to restriction measures,we analysed CO_(2) flux data obtained using the eddy covariance technique from 2015 to 2020,and compared CO_(2) flux during the COVID-19 confinement period in 2020 with the preceding years(2015-2019)and across various levels of confinement.The results showed that:(1)the annual CO_(2) flux was 2.1±0.2 kg C/(m^(2)·yr)in 2020,which showed a significant reduction of 31.8%compared to the adjacent 2019;(2)the reduction in CO_(2) flux was closely related to the level of restrictions on human activities;(3)most reductions occurred during the morning(85.7%)and evening(32.7%)peak traffic times,indicating that commuting-related transportation is a primary contributor to urban CO_(2) emissions.It is suggested that measures that reduce transportation-related CO_(2) sources should be considered as priorities for reducing urban CO_(2) emissions.The dynamic variation of urban CO_(2) flux captured by the eddy covariance technology is conductive to strengthening the supervision of the implementation of urban carbon emission reduction policies,promoting the achievement of dual carbon goals.展开更多
Over the past few decades, numerous adaptive Kalman filters(AKFs) have been proposed. However, achieving online estimation with both high estimation accuracy and fast convergence speed is challenging, especially when ...Over the past few decades, numerous adaptive Kalman filters(AKFs) have been proposed. However, achieving online estimation with both high estimation accuracy and fast convergence speed is challenging, especially when both the process noise and measurement noise covariance matrices are relatively inaccurate. Maximum likelihood estimation(MLE) possesses the potential to achieve this goal, since its theoretical accuracy is guaranteed by asymptotic optimality and the convergence speed is fast due to weak dependence on accurate state estimation.Unfortunately, the maximum likelihood cost function is so intricate that the existing MLE methods can only simply ignore all historical measurement information to achieve online estimation,which cannot adequately realize the potential of MLE. In order to design online MLE-based AKFs with high estimation accuracy and fast convergence speed, an online exploratory MLE approach is proposed, based on which a mini-batch coordinate descent noise covariance matrix estimation framework is developed. In this framework, the maximum likelihood cost function is simplified for online estimation with fewer and simpler terms which are selected in a mini-batch and calculated with a backtracking method. This maximum likelihood cost function is sidestepped and solved by exploring possible estimated noise covariance matrices adaptively while the historical measurement information is adequately utilized. Furthermore, four specific algorithms are derived under this framework to meet different practical requirements in terms of convergence speed, estimation accuracy,and calculation load. Abundant simulations and experiments are carried out to verify the validity and superiority of the proposed algorithms as compared with existing state-of-the-art AKFs.展开更多
In order to obtain better inverse synthetic aperture radar(ISAR)image,a novel structure-enhanced spatial spectrum is proposed for estimating the incoherence parameters and fusing multiband.The proposed method takes fu...In order to obtain better inverse synthetic aperture radar(ISAR)image,a novel structure-enhanced spatial spectrum is proposed for estimating the incoherence parameters and fusing multiband.The proposed method takes full advantage of the original electromagnetic scattering data and its conjugated form by combining them with the novel covariance matrices.To analyse the superiority of the modified algorithm,the mathematical expression of equivalent signal to noise ratio(SNR)is derived,which can validate our proposed algorithm theoretically.In addition,compared with the conventional matrix pencil(MP)algorithm and the conventional root-multiple signal classification(Root-MUSIC)algorithm,the proposed algorithm has better parameter estimation performance and more accurate multiband fusion results at the same SNR situations.Validity and effectiveness of the proposed algorithm is demonstrated by simulation data and real radar data.展开更多
基金supported by National Natural Science Foundation of China (42001305)Guangdong Basic and Applied Basic Research Foundation (2022A1515011459)+3 种基金GDAS'Special Project of Science and Technology Development (2020GDASYL-20200102001)Guangzhou Basic and Applied Basic Research Foundation (2023A04J1534) to Z.W.the US National Science Foundation (NSF) Macrosystems Biology and NEON-Enabled Science grant 1638720 to P.A.T.,and E.L.K.NSF Biology Integration Institute award ASCEND,DBI-2021898 to P.A.T.
文摘Characterizing foliar trait variation in sun and shade leaves can provide insights into inter-and intra-species resource use strategies and plant response to environmental change.However,datasets with records of multiple foliar traits from the same individual and including shade leaves are sparse,which limits our ability to investigate trait-trait,trait-environment relationships and trait coordination in both sun and shade leaves.We presented a comprehensive dataset of 15 foliar traits from sun and shade leaves sampled with leaf spectroscopy,including 424 individuals of 110 plant species from 19 sites across eastern North America.We investigated trait variation,covariation,scaling relationships with leaf mass,and the effects of environment,canopy position,and taxonomy on trait expression.Generally,sun leaves had higher leaf mass per area,nonstructural carbohydrates and total phenolics,lower mass-based chlorophyll a+b,carotenoids,phosphorus,and potassium,but exhibited species-specific characteristics.Covariation between sun and shade leaf traits,and trait-environment relationships were overall consistent across species.The main dimensions of foliar trait variation in seed plants were revealed including leaf economics traits,photosynthetic pigments,defense,and structural traits.Taxonomy and canopy position collectively explained most of the foliar trait variation.This study highlights the importance of including intra-individual and intra-specific trait variation to improve our understanding of ecosystem functions.Our findings have implications for efficient field sampling,and trait mapping with remote sensing.
基金partly financially supported by a Grant-in-Aid for Scientific Research from the Japan Society for the Promotion of Science(Grant No.20K05762)China Scholarship Council(Grant No.201806990031)。
文摘Ionomic profiles are primarily influenced by genetic and environmental factors.Identifying ionomic responses to varietal effects is necessary to understand the ionomic variations among species or subspecies and to potentially understand genetic effects on ionomic profiles.We cultivated 120 rice(Oryza sativa)varieties to seedling stage in identical hydroponic conditions and determined the concentrations of 26 elements(including 3 anions)in the shoots and roots of rice.Although the subspecies effects were limited by the genus Oryza pre-framework and its elemental chemical properties,we found significant differences in ionomic variations in most elements among the aus,indica and japonica subspecies.Principal component analysis of the correlations indicated that variations in the root-to-shoot ionomic transport mechanisms were the main causes of ionomic differences among the subspecies.Furthermore,the correlations were primarily associated with the screening of varieties for elemental covariation effects that can facilitate breeding biofortified rice varieties with safe concentrations of otherwise toxic elements.The japonica subspecies exhibited the strongest elemental correlations and elemental covariation effects,therefore,they showed greater advantages for biofortification than the indica and aus subspecies,whereas indica and aus subspecies were likely safer in metal(loid)polluted soils.We also found that geographical and historical distribution significantly defined the ionomic profiles.Overall,the results of this study provided a reference for further association studies to improve the nutritional status and minimize toxicity risks in rice production.
基金National Natural Science Foundation of China(4133042541175062)
文摘By employing the singular value decomposition(SVD) analysis, we have investigated in the present paper the covariations between circulation changes in the Northern(NH) and Southern Hemispheres(SH) and their associations with ENSO by using the NCEP/NCAR reanalysis, the reconstructed monthly NOAA SST, and CMAP precipitation along with NOAA Climate Prediction Center(CPC) ENSO indices. A bi-hemispheric covariation mode(hereafter BHCM) is explored, which is well represented by the first mode of the SVD analysis of sea surface pressure anomaly(SLPA-SVD1). This SVD mode can explain 57.36% of the total covariance of SLPA. BHCM varies in time with a long-term trend and periodicities of 3—5 years. The long term trend revealed by SVD1 shows that the SLP increases in the equatorial central and eastern Pacific but decreases in the western Pacific and tropical Indian Ocean, which facilitates easterlies in the lower troposphere to be intensified and El Ni觡o events to occur with lower frequency. The spatial pattern of the BHCM looks roughly symmetric about the equator in the tropics, whereas it is characterized by zonal disturbances in the mid-latitude of NH and is highly associated with AAO in the mid-latitude of SH. On inter-annual time scales, the BHCM is highly correlated with ENSO. The atmosphere in both the NH and SH responds to sea surface temperature anomalies in the equatorial region, while the contemporaneous circulation changes in the NH and SH in turn affect the occurrence of El Ni觡o/La Ni觡a. In boreal winter, significant temperature and precipitation anomalies associated with the BHCM are found worldwide. Specifically, in the positive phase of the BHCM,temperature and precipitation are anomalously low in eastern China and some other regions of East Asia. These results are helpful for us to better understand interactions between circulations in the NH and SH and the dynamical mechanisms behind these interactions.
文摘A previous study, focused on the correlation of muta-tion pairs of synonymous (S) and asynonymous (A) mutations, distinguished only between the treated and untreated data of protease and reverse tran-scriptase (RT) of HIV-1 subtype B. It is well known that single mutation patterns in HIV-1 are treat-ment-specific. It logically follows that covariation between mutations will also be treatment specific. Thus, our motivation is to give a more in depth study of the covariation between mutation pairs, analyzing not only treated and untreated, but what specific treatments were used, and how they affected the co-variation between the mutations differently. We in-tended to further deepen this study by analyzing the covariation of mutations in protease and RT in dif-ferent subtypes of HIV-1. We found that virus sam-ples subjected to antiretroviral Protease- and RT- inhibitors do show different patterns of mutation covariation in B-subtype protease and RT of HIV-1, while maintaining the same overall trend. covariation will tend to be higher and more distinct from and covariation after treatment. The same trend continues in protease and RT re-gardless of subtype. We also found the highly cova-ried codon positions, position pairs, and position- covariation clusters in protease, affected by different treatments. Most of them are well known major drug-resistance sites for these treatments.
文摘We introduce the notion of symmetric covariation,which is a new measure of dependence between two components of a symmetricα-stable random vector,where the stability parameterαmeasures the heavy-tailedness of its distribution.Unlike covariation that exists only whenα∈(1,2],symmetric covariation is well defined for allα∈(0,2].We show that symmetric covariation can be defined using the proposed generalized fractional derivative,which has broader usages than those involved in this work.Several properties of symmetric covariation have been derived.These are either similar to or more general than those of the covariance functions in the Gaussian case.The main contribution of this framework is the representation of the characteristic function of bivariate symmetricα-stable distribution via convergent series based on a sequence of symmetric covariations.This series representation extends the one of bivariate Gaussian.
基金supported by Natural Science Foundation of Hu'nan Province(2024JJ5409)。
文摘In underwater target search path planning,the accuracy of sonar models directly dictates the accurate assessment of search coverage.In contrast to physics-informed sonar models,traditional geometric sonar models fail to accurately characterize the complex influence of marine environments.To overcome these challenges,we propose an acoustic physics-informed intelligent path planning framework for underwater target search,integrating three core modules:The acoustic-physical modeling module adopts 3D ray-tracing theory and the active sonar equation to construct a physics-driven sonar detection model,explicitly accounting for environmental factors that influence sonar performance across heterogeneous spaces.The hybrid parallel computing module adopts a message passing interface(MPI)/open multi-processing(Open MP)hybrid strategy for large-scale acoustic simulations,combining computational domain decomposition and physics-intensive task acceleration.The search path optimization module adopts the covariance matrix adaptation evolution algorithm to solve continuous optimization problems of heading angles,which ensures maximum search coverage for targets.Largescale experiments conducted in the Pacific and Atlantic Oceans demonstrate the framework's effectiveness:(1)Precise capture of sonar detection range variations from 5.45 km to 50 km in heterogeneous marine environments.(2)Significant speedup of 453.43×for acoustic physics modeling through hybrid parallelization.(3)Notable improvements of 7.23%in detection coverage and 15.86%reduction in optimization time compared to the optimal baseline method.The framework provides a robust solution for underwater search missions in complex marine environments.
基金sponsored by the National Natural Science Foundation of China[grant number U2442218]。
文摘Land surface temperature(LST)is the key variable in land-atmosphere interaction,having an important impact on weather and climate forecasting.However,achieving consistent analysis of LST and the atmosphere in assimilation is quite challenging.This is because there is limited knowledge about the cross-component background error covariance(BEC)between LST and atmospheric state variables.This study aims to clarify whether there is a relationship between the error of LST and atmospheric variables,and whether this relationship varies spatially and temporally.To this end,the BEC coupled with atmospheric variables and LST was constructed(LST-BEC),and its characteristics were analyzed based on the 2023 mei-yu season.The general characteristics of LST-BEC show that the LST is mainly correlated with the atmospheric temperature and the correlation decreases gradually with a rise in atmospheric height,and the error standard deviation of the LST is noticeably larger than that of the low-level atmospheric temperature.The spatiotemporal characteristics of LST-BEC on the heavy-rain day and light-rain day show that the error correlation and error standard deviation of LST and low-level atmospheric temperature and humidity are closely related to the weather background,and also have obvious diurnal variations.These results provide valuable information for strongly coupled land-atmosphere assimilation.
基金supported in part by the National Natural Science Foundation of China(12171124,61933007)the Natural Science Foundation of Heilongjiang Province of China(ZD2022F003)+2 种基金the National High-End Foreign Experts Recruitment Plan of China(G2023012004L)the Royal Society of UKthe Alexander von Humboldt Foundation of Germany
文摘In this paper, the problem of cubature Kalman fusion filtering(CKFF) is addressed for multi-sensor systems under amplify-and-forward(AaF) relays. For the purpose of facilitating data transmission, AaF relays are utilized to regulate signal communication between sensors and filters. Here, the randomly varying channel parameters are represented by a set of stochastic variables whose occurring probabilities are permitted to exhibit bounded uncertainty. Employing the spherical-radial cubature principle, a local filter under AaF relays is initially constructed. This construction ensures and minimizes an upper bound of the filtering error covariance by designing an appropriate filter gain. Subsequently, the local filters are fused through the application of the covariance intersection fusion rule. Furthermore, the uniform boundedness of the filtering error covariance's upper bound is investigated through establishing certain sufficient conditions. The effectiveness of the proposed CKFF scheme is ultimately validated via a simulation experiment concentrating on a three-phase induction machine.
基金supported in part by the National Natural Science Foundation of China(Grant Nos.12435006,12435007,12475117,12141501,and 123B2080)the National Key R&D Program of China(Grant No.2024YFE0109803)the National Key Laboratory of Neutron Science and Technology(Grant No.NST202401016)。
文摘The simultaneous description for nuclear matter and finite nuclei has been a long-standing challenge in nuclear ab initio theory.With the success for nuclear matter,the relativistic Brueckner-Hartree-Fock(RBHF)theory with covariant chiral interactions is a promising ab initio approach to describe both nuclear matter and finite nuclei.In the description of finite nuclei with the current RBHF theory,the covariant chiral interactions have to be localized to make calculations feasible.In order to examine the reliability and validity,in this letter,the RBHF theory with local and nonlocal covariant chiral interactions at leading order is applied to nuclear matter.The low-energy constants in the covariant chiral interactions determined with the local regularization are close to those with the nonlocal regularization.Moreover,the RBHF theory using covariant chiral interactions with local and nonlocal regulators provides an equally good description of the saturation properties of nuclear matter.The present work paves the way for the implementation of covariant chiral interactions in RBHF theory for finite nuclei.
文摘本文主要对Hessian度量诱导的截面曲率展开分析。首先阐述Hessian度量和与之相关的Christoffor符号和曲率张量公式。接着介绍当定义域U为锥时,齐次函数的相关概念,以及Clebsch covariant S(f)和截面曲率的联系。利用R3中开子集U和超平面M = {f = 1}相切2-平面上一点处的截面曲率可由S(f)和Hessian行列式H(f)表示,其中f为R3上的齐次多项式,得到不变量S(f)为零与Witten-Dijkgraaf-Verlinde-Verlinde (WDVV)方程等价的条件,即f可以表示为两种特殊的形式。This paper primarily analyzes the sectional curvature induced by the Hessian metric. It begins by detailing the Hessian metric and its associated Christoffel symbols and curvature tensor formulas. It then introduces the concept of homogeneous functions when the domain U is a cone, as well as the relationship between the Clebsch covariant S(f) and the sectional curvature. Using an open subset U in R3 and a hypersurface M = {f = 1}, the sectional curvature at a point on a 2-plane tangent to M can be expressed in terms of S(f) and the Hessian matrix H(f), where f is a homogeneous polynomial on R3. The condition for the invariant S(f) to be zero is equivalent to the Witten-Dijkgraaf-Verlinde-Verlinde (WDVV) equations, which implies that f can be represented in two specific forms.
基金supported by the MSIT(Ministry of Science and ICT),Republic of Korea,under the Convergence Security Core Talent Training Business Support Program(IITP-2025-RS-2023-00266605)supervised by the IITP(Institute for Information&Communications Technology Planning&Evaluation).
文摘Surveillance systems can take various forms,but gait-based surveillance is emerging as a powerful approach due to its ability to identify individuals without requiring their cooperation.In the existing studies,several approaches have been suggested for gait recognition;nevertheless,the performance of existing systems is often degraded in real-world conditions due to covariate factors such as occlusions,clothing changes,walking speed,and varying camera viewpoints.Furthermore,most existing research focuses on single-person gait recognition;however,counting,tracking,detecting,and recognizing individuals in dual-subject settings with occlusions remains a challenging task.Therefore,this research proposed a variant of an automated gait model for occluded dual-subject walk scenarios.More precisely,in the proposed method,we have designed a deep learning(DL)-based dual-subject gait model(DSG)involving three modules.The first module handles silhouette segmentation,localization,and counting(SLC)using Mask-RCNN with MobileNetV2.The next stage uses a Convolutional block attention module(CBAM)-based Siamese network for frame-level tracking with a modified gallery setting.Following the last,gait recognition based on regionbased deep learning is proposed for dual-subject gait recognition.The proposed method,tested on Shri Mata Vaishno Devi University(SMVDU)-Multi-Gait and Single-Gait datasets,shows strong performance with 94.00%segmentation,58.36%tracking,and 63.04%gait recognition accuracy in dual-subject walk scenarios.
基金Supported by the Grant of National Science Foundation of China(11971433)Zhejiang Gongshang University“Digital+”Disciplinary Construction Management Project(SZJ2022B004)+1 种基金Institute for International People-to-People Exchange in Artificial Intelligence and Advanced Manufacturing(CCIPERGZN202439)the Development Fund for Zhejiang College of Shanghai University of Finance and Economics(2023FZJJ15).
文摘As data becomes increasingly complex,measuring dependence among variables is of great interest.However,most existing measures of dependence are limited to the Euclidean setting and cannot effectively characterize the complex relationships.In this paper,we propose a novel method for constructing independence tests for random elements in Hilbert spaces,which includes functional data as a special case.Our approach is using distance covariance of random projections to build a test statistic that is computationally efficient and exhibits strong power performance.We prove the equivalence between testing for independence expressed on the original and the projected covariates,bridging the gap between measures of testing independence in Euclidean spaces and Hilbert spaces.Implementation of the test involves calibration by permutation and combining several p-values from different projections using the false discovery rate method.Simulation studies and real data examples illustrate the finite sample properties of the proposed method under a variety of scenarios.
基金supported by the National Natural Science Foundation of China under Grants No.61671367 and 62471381the Research Foundation of Science and Technology on Communication Networks Laboratory,and the National Key Laboratory of Wireless Communications Foundation under Grant No.IFN202401.
文摘Satellite communication plays an important role in 6G systems.However,satellite communication systems are more susceptible to intentional or unintentional interference signals than other communication systems because of their working mechanism of transparent forwarding.For the purpose of eliminating the influence of interference,this paper develops an angle reciprocal interference suppression scheme based on the reconstruction of interferenceplus-noise covariance matrix(ARIS-RIN).Firstly,we utilize the reciprocity between the known beam central angle and the unknown signal arrival angle to estimate the angle of arrival(AOA)of desired signal due to the multi-beam coverage.Then,according to the priori known spatial spectrum distribution,the interferenceplus-noise covariance matrix(INCM)is reconstructed by integrating within the range except the direction of desired signal.In order to correct the estimation bias of the first two steps,the worst-case performance optimization technology is adopted in the process of solving the beamforming vector.Numerical simulation results show that the developed scheme:1)has a higher output signal-to-interference-plus-noise ratio(SINR)under arbitrary signal-to-noise ratio(SNR);2)still has good performance under small snapshots;3)is robuster and easier to be realized when comparing with minimum variance distortionless response(MVDR)and the traditional diagonal loading algorithms.
基金supported by the National Natural Science Foundation of China(Nos.62120106003 and 62173301)。
文摘The reuse of liquid propellant rocket engines has increased the difficulty of their control and estimation.State and parameter Moving Horizon Estimation(MHE)is an optimization-based strategy that provides the necessary information for model predictive control.Despite the many advantages of MHE,long computation time has limited its applications for system-level models of liquid propellant rocket engines.To address this issue,we propose an asynchronous MHE method called advanced-multi-step MHE with Noise Covariance Estimation(amsMHE-NCE).This method computes the MHE problem asynchronously to obtain the states and parameters and can be applied to multi-threaded computations.In the background,the state and covariance estimation optimization problems are computed using multiple sampling times.In real-time,sensitivity is used to quickly approximate state and parameter estimates.A covariance estimation method is developed using sensitivity to avoid redundant MHE problem calculations in case of sensor degradation during engine reuse.The amsMHE-NCE is validated through three cases based on the space shuttle main engine system-level model,and we demonstrate that it can provide more accurate real-time estimates of states and parameters compared to other commonly used estimation methods.
基金funded by Vietnam National University,Ho Chi Minh City(VNU-HCM)under grant number C2022-18-15.
文摘The Mekong Delta in Vietnam is a region that produces rice and emits methane,a potent greenhouse gas.Vietnam’s rice exports,which rank among the top four globally,have a significant impact on the world’s food suppy.The Eddy Covariance system,positioned in the rice field,has been recording methane emission rates and bio-meteorological factors.This study presents the findings of three crop seasons(Summer-Autumn 2020(S-A20),Winter-Spring 2021(W-S21),and Spring-Summer 2021(S-S21))from the year 2020 to 2021.The highest CH4 emission value was observed in the S-S21 crop,reaching 4.14μmol s^(-1 )m^(-2).Elevated CH_(4) emission rates were predominantly recorded during the vegetative stage within first 21 days after planting,while lower CH_(4) emissions were observed during the reproductive and ripening stages.This pattern clearly indicates higher methane emissions at the vegetative stage of the growing rice,likely due to the abundance of organic matter in the rice fields.The average CH4 emission rate was 0.1μmol m^(-2) s^(-1).Notably,high methane emissions were recorded when the soil surface temperature was below 33℃.As a results,the S-S21 exhibits the highest methane emission rates compared to other seasons.
基金funded by the National Natural Science Foundation of China (Grant No. 62471048)Open Fund Project of Beijing Key Laboratory of High Dynamic Navigation TechnologyKey Laboratory Fund Project of Modern Measurement and Control Technology, Ministry of Education
文摘In complex environments such as high dynamics and weak signals,a satellite signal compensation method based on prefabricated trajectory assistance and an improved adaptive Kalman filter is proposed for a 155 mm differential rotating rear-body control-guided projectile to address the situation of satellite signal flickering and loss in projectile navigation systems due to environmental limitations.First,establish the system state and measurement equation when receiving satellite signals normally.Second,a seven-degree-of-freedom external ballistic model is constructed,and the ideal trajectory output from the ballistic model is used to provide the virtual motion state of the projectile,which is input into a filter as a substitute observation when satellite signals are lost.Finally,an adaptive Kalman filter(AKF)is designed,the proposed adaptive Kalman filter can accurately adjust the estimation error covariance matrix and Kalman gain in real-time based on information covariance mismatch.The simulation results show that compared to the classical Kalman filter,it can reduce the average positioning error by more than 38.21%in the case of short-term and full-range loss of satellite signals,providing a new idea for the integrated navigation of projectiles with incomplete information under the condition of satellite signal loss.
基金National Natural Science Foundation of China under Grant 42276055National Key Research and Development Program under Grant 2022YFC2803503Fundamental Research Funds for the Central Universities under Grant 202262008.
文摘Full waveform inversion methods evaluate the properties of subsurface media by minimizing the misfit between synthetic and observed data.However,these methods omit measurement errors and physical assumptions in modeling,resulting in several problems in practical applications.In particular,full waveform inversion methods are very sensitive to erroneous observations(outliers)that violate the Gauss–Markov theorem.Herein,we propose a method for addressing spurious observations or outliers.Specifically,we remove outliers by inverting the synthetic data using the local convexity of the Gaussian distribution.To achieve this,we apply a waveform-like noise model based on a specific covariance matrix definition.Finally,we build an inversion problem based on the updated data,which is consistent with the wavefield reconstruction inversion method.Overall,we report an alternative optimization inversion problem for data containing outliers.The proposed method is robust because it uses uncertainties.This method enables accurate inversion,even when based on noisy models or a wrong wavelet.
基金supported by the National Key Research Program of China(No.2017YFE0127700)the National Natural Science Foundation of China(No.42301325)China Postdoctoral Science Foundation(Nos.2023M743704 and 2024T170975)。
文摘Urban areas are the major anthropogenic source of atmospheric CO_(2),thus making longterm and continuous observations of their carbon emission dynamics extremely important.The COVID-19 lockdown served as a natural experiment that provided a unique opportunity to analyse the contribution of human activities to CO_(2) emissions from urban areas.In 2020,Beijing experienced COVID-19 confinement with different levels of restrictions on social mobility and economic activity,resulting in reductions in CO_(2) emissions.To investigate the response mechanisms of CO_(2) flux to restriction measures,we analysed CO_(2) flux data obtained using the eddy covariance technique from 2015 to 2020,and compared CO_(2) flux during the COVID-19 confinement period in 2020 with the preceding years(2015-2019)and across various levels of confinement.The results showed that:(1)the annual CO_(2) flux was 2.1±0.2 kg C/(m^(2)·yr)in 2020,which showed a significant reduction of 31.8%compared to the adjacent 2019;(2)the reduction in CO_(2) flux was closely related to the level of restrictions on human activities;(3)most reductions occurred during the morning(85.7%)and evening(32.7%)peak traffic times,indicating that commuting-related transportation is a primary contributor to urban CO_(2) emissions.It is suggested that measures that reduce transportation-related CO_(2) sources should be considered as priorities for reducing urban CO_(2) emissions.The dynamic variation of urban CO_(2) flux captured by the eddy covariance technology is conductive to strengthening the supervision of the implementation of urban carbon emission reduction policies,promoting the achievement of dual carbon goals.
基金supported in part by the National Key Research and Development Program of China(2023YFB3906403)the National Natural Science Foundation of China(62373118,62173105)the Natural Science Foundation of Heilongjiang Province of China(ZD2023F002)
文摘Over the past few decades, numerous adaptive Kalman filters(AKFs) have been proposed. However, achieving online estimation with both high estimation accuracy and fast convergence speed is challenging, especially when both the process noise and measurement noise covariance matrices are relatively inaccurate. Maximum likelihood estimation(MLE) possesses the potential to achieve this goal, since its theoretical accuracy is guaranteed by asymptotic optimality and the convergence speed is fast due to weak dependence on accurate state estimation.Unfortunately, the maximum likelihood cost function is so intricate that the existing MLE methods can only simply ignore all historical measurement information to achieve online estimation,which cannot adequately realize the potential of MLE. In order to design online MLE-based AKFs with high estimation accuracy and fast convergence speed, an online exploratory MLE approach is proposed, based on which a mini-batch coordinate descent noise covariance matrix estimation framework is developed. In this framework, the maximum likelihood cost function is simplified for online estimation with fewer and simpler terms which are selected in a mini-batch and calculated with a backtracking method. This maximum likelihood cost function is sidestepped and solved by exploring possible estimated noise covariance matrices adaptively while the historical measurement information is adequately utilized. Furthermore, four specific algorithms are derived under this framework to meet different practical requirements in terms of convergence speed, estimation accuracy,and calculation load. Abundant simulations and experiments are carried out to verify the validity and superiority of the proposed algorithms as compared with existing state-of-the-art AKFs.
文摘In order to obtain better inverse synthetic aperture radar(ISAR)image,a novel structure-enhanced spatial spectrum is proposed for estimating the incoherence parameters and fusing multiband.The proposed method takes full advantage of the original electromagnetic scattering data and its conjugated form by combining them with the novel covariance matrices.To analyse the superiority of the modified algorithm,the mathematical expression of equivalent signal to noise ratio(SNR)is derived,which can validate our proposed algorithm theoretically.In addition,compared with the conventional matrix pencil(MP)algorithm and the conventional root-multiple signal classification(Root-MUSIC)algorithm,the proposed algorithm has better parameter estimation performance and more accurate multiband fusion results at the same SNR situations.Validity and effectiveness of the proposed algorithm is demonstrated by simulation data and real radar data.
