Global navigation satellite system-reflection(GNSS-R)sea surface altimetry based on satellite constellation platforms has become a new research direction and inevitable trend,which can meet the altimetric precision at...Global navigation satellite system-reflection(GNSS-R)sea surface altimetry based on satellite constellation platforms has become a new research direction and inevitable trend,which can meet the altimetric precision at the global scale required for underwater navigation.At present,there are still research gaps for GNSS-R altimetry under this mode,and its altimetric capability cannot be specifically assessed.Therefore,GNSS-R satellite constellations that meet the global altimetry needs to be designed.Meanwhile,the matching precision prediction model needs to be established to quantitatively predict the GNSS-R constellation altimetric capability.Firstly,the GNSS-R constellations altimetric precision under different configuration parameters is calculated,and the mechanism of the influence of orbital altitude,orbital inclination,number of satellites and simulation period on the precision is analyzed,and a new multilayer feedforward neural network weighted joint prediction model is established.Secondly,the fit of the prediction model is verified and the performance capability of the model is tested by calculating the R2 value of the model as 0.9972 and the root mean square error(RMSE)as 0.0022,which indicates that the prediction capability of the model is excellent.Finally,using the novel multilayer feedforward neural network weighted joint prediction model,and considering the research results and realistic costs,it is proposed that when the constellation is set to an orbital altitude of 500 km,orbital inclination of 75and the number of satellites is 6,the altimetry precision can reach 0.0732 m within one year simulation period,which can meet the requirements of underwater navigation precision,and thus can provide a reference basis for subsequent research on spaceborne GNSS-R sea surface altimetry.展开更多
Spaceborne antennas are essential for remote sensing,deep-space communication,and Earth observation,yet their trajectory planning is complicated by nonlinear base-manipulator coupling and antenna flexibility.To addres...Spaceborne antennas are essential for remote sensing,deep-space communication,and Earth observation,yet their trajectory planning is complicated by nonlinear base-manipulator coupling and antenna flexibility.To address these challenges,this paper proposes a multi-objective trajectory optimization framework.The system dynamics capture both nonlinear rigid-flexible coupling and antenna deformation through a reduced-order formulation.To enhance discretization efficiency,a predictive-terminal hp-adaptive pseudospectral method is employed,assigning collocation density based on task-phase characteristics:finer resolution is applied to dynamic segments requiring higher accuracy,especially near the terminal phase.This enables efficient transcription of the continuous-time problem into a Nonlinear Programming Problem(NLP).The resulting NLP is then solved using a multi-objective optimization strategy based on the nondominated sorting genetic algorithm II,which explores trade-offs among antenna pointing accuracy,energy consumption,and structural vibration.Numerical results demonstrate that the proposed method achieves a reduction of approximately 14.0% in control energy and 41.8%in peak actuation compared to a GPOPS-II baseline,while significantly enhancing vibration suppression.The resulting Pareto front reveals structured trade-offs and clustered solutions,offering robust and diverse options for precision,low-disturbance mission planning.展开更多
The spaceborne inverse synthetic aperture radar(ISAR)has attracted significant attention due to its extensive observation range and imaging performance.However,the complex motion between spaceborne platform and the ai...The spaceborne inverse synthetic aperture radar(ISAR)has attracted significant attention due to its extensive observation range and imaging performance.However,the complex motion between spaceborne platform and the air target leads to complex signal modulation.The scattering anisotropy and occlusion lead to the scattering points missing problem.These problems pose a serious challenge to the traditional ISAR imaging algorithms.Aiming at the above problems,this paper proposes a joint intra-frame and inter-frame imaging algorithm based on spaceborne ISAR.In this algorithm,we divide the long coherent processing interval into several sub-apertures using the narrow-band tracking data,and each-order terms of the signal within sub-aperture is derived in detail.Then,the intra-frame algorithm based on the parametric minimized image entropy search is proposed to correct the spatial-variant phase errors caused by the complex relative motion.As to the well-focused images from different views and the rotation parameters obtained in sub-apertures,the inter-frame algorithm based on wavelet transform can perform image registration and image fusion to obtain more detailed target feature information and more complete target structure.In simulated and real-measured data experiments,the effectiveness and superiority of the proposed algorithm are validated.展开更多
Accurate digital terrain models(DTMs)are essential for a wide range of geospatial and environmental applications,yet their derivation in forested regions remains a significant challenge.Existing global DTMs,typically ...Accurate digital terrain models(DTMs)are essential for a wide range of geospatial and environmental applications,yet their derivation in forested regions remains a significant challenge.Existing global DTMs,typically generated from satellite stereo photogrammetry or interferometric synthetic aperture radar(InSAR),fail to accurately capture understory terrain due to limited penetration capabilities,resulting in elevation overestimation in densely vegetated areas.While airborne light detection and ranging(LiDAR)can provide high-accuracy DTMs,its limited spatial coverage and high acquisition cost hinder large-scale applications.Thus,there is an urgent need for a scalable and cost-effective approach to extract DTMs directly from satellite-derived digital surface models(DSMs).In this study,we propose a simple,interpretable understory terrain extraction method that utilizes canopy height data from Global Ecosystem Dynamics Investigation(GEDI)and Ice,Cloud,and Land Elevation Satellite-2(ICESat-2)to construct a tree height surface model,which is then subtracted from the stereo-derived DSM to generate the final DTM.By directly incorporating LiDAR constraints,the method avoids error propagation from multiple heterogeneous datasets and reduces reliance on ancillary inputs,ensuring ease of implementation and broad applicability.In contrast to machine learning-based terrain modeling methods,which are often prone to overfitting and data bias,the proposed approach is simple,interpretable,and robust across diverse forested landscapes.The accuracy of the resulting DTM was validated against airborne LiDAR reference data and compared with both the Copernicus Digital Elevation Model(DEM)and the forest and buildings removed DEM(FABDEM),a global bare-earth elevation model corrected for vegetation bias.The results indicate that the proposed DTM consistently outperforms the Copernicus DEM(CopDEM)and achieves accuracy comparable to FABDEM.In addition,its finer spatial resolution of 1 m,compared to the 30 m resolution of FABDEM,allows for more detailed terrain representation and better capture of fine-scale variation.This advantage is most pronounced in gently to moderately sloped areas,where the proposed DTM shows clearly higher accuracy than both the CopDEM and FABDEM.The results confirm that high-resolution DTMs can be effectively extracted from DSMs using spaceborne LiDAR constraints,offering a scalable solution for terrain modeling in forested environments where airborne LiDAR is unavailable.To illustrate the potential utility of the proposed DTM,we applied it to a fire risk mapping application based on topographic parameters such as slope,aspect,and elevation.This case highlights how improved terrain representation can support geospatial hazard assessments.展开更多
Spaceborne global navigation satellite system-reflectometry has become an effective technique for Soil Moisture(SM)retrieval.However,the accuracy of global SM retrieval using a single model is limited due to the compl...Spaceborne global navigation satellite system-reflectometry has become an effective technique for Soil Moisture(SM)retrieval.However,the accuracy of global SM retrieval using a single model is limited due to the complexity of land surface.Introducing redundant ancillary data may also result in over-reliance problems.Therefore,we propose a method for SM retrieval that considers geographical disparities using the data from Cyclone GNSS(CYGNSS)obser-vations and Soil Moisture Active and Passive(SMAP)product.Based on the CYGNSS effective reflectivity and ancillary datasets of SMAP,we establish five models for each grid with different parameters to achieve global SM retrieval.Subsequently,an optimal model,determined by the performance indicator,is used for SM retrieval.The results show that the root mean square error SRMsE with the improved methodis decreased by 9.1%using SMAP SM as reference with the SRMsE=0.040 cm3/cm3 compared with using single reflectivity-temperature-vegetation method.Additionally,using the in-situ SM of International Soil Moisture Network as reference,the overall correlation coeffcient R and SRMSE values with the improved method are 0.80 and 0.064 cm3/cm3,respectively.The average R of the chosen sites is increased by 22.7%,and the average SRMse is decreased by 8.7%.The results indicate that the improved method can better retrieve SM in both global and local scales without redundant auxiliary data.展开更多
Global Navigation Satellite System Reflectometry(GNSS-R)remote sensing has demonstrated broad application potential in marine oil spill monitoring due to its advantages such as all-weather capability,wide spatial cove...Global Navigation Satellite System Reflectometry(GNSS-R)remote sensing has demonstrated broad application potential in marine oil spill monitoring due to its advantages such as all-weather capability,wide spatial coverage,and high spatiotemporal resolution.However,the morphological features of Delay-Doppler Map(DDM)provided by GNSSR are simultaneously influenced by factors such as wind speed and surface oil films,leading to high false positive rates in traditional oil spill detection methods.To address this challenge,this study proposes a multimodal recognition framework that integrates both DDM imagery and wind speed information.A dual-branch Residual Network(Res Net)-based feature extraction network is designed to extract high-dimensional features from DDM and wind speed data,which are then fused at the feature level to enable accurate identification of oil-contaminated areas.To alleviate the scarcity of labeled real-world samples,a large-scale synthetic multimodal dataset is generated based on the ZavorotnyVoronovich(Z-V)scattering model for pretraining,followed by transfer learning using a small number of real GNSS-R observations to enhance the model's adaptability to real marine environments.Experimental results show that the proposed method achieves 91%classification accuracy on the test set,significantly outperforming baseline models such as Convolutional Neural Networks(CNNs).Furthermore,the incorporation of wind speed as an auxiliary modality effectively reduces false positives caused by wind-wave interference and enhances the model's sensitivity to oil film signatures.This study provides a feasible theoretical foundation and technical pathway for the application of GNSS-R in marine oil spill detection.展开更多
基金the National Natural Science Foundation of China under Grant(42274119)the Liaoning Revitalization Talents Program under Grant(XLYC2002082)+1 种基金National Key Research and Development Plan Key Special Projects of Science and Technology Military Civil Integration(2022YFF1400500)the Key Project of Science and Technology Commission of the Central Military Commission.
摘要Global navigation satellite system-reflection(GNSS-R)sea surface altimetry based on satellite constellation platforms has become a new research direction and inevitable trend,which can meet the altimetric precision at the global scale required for underwater navigation.At present,there are still research gaps for GNSS-R altimetry under this mode,and its altimetric capability cannot be specifically assessed.Therefore,GNSS-R satellite constellations that meet the global altimetry needs to be designed.Meanwhile,the matching precision prediction model needs to be established to quantitatively predict the GNSS-R constellation altimetric capability.Firstly,the GNSS-R constellations altimetric precision under different configuration parameters is calculated,and the mechanism of the influence of orbital altitude,orbital inclination,number of satellites and simulation period on the precision is analyzed,and a new multilayer feedforward neural network weighted joint prediction model is established.Secondly,the fit of the prediction model is verified and the performance capability of the model is tested by calculating the R2 value of the model as 0.9972 and the root mean square error(RMSE)as 0.0022,which indicates that the prediction capability of the model is excellent.Finally,using the novel multilayer feedforward neural network weighted joint prediction model,and considering the research results and realistic costs,it is proposed that when the constellation is set to an orbital altitude of 500 km,orbital inclination of 75and the number of satellites is 6,the altimetry precision can reach 0.0732 m within one year simulation period,which can meet the requirements of underwater navigation precision,and thus can provide a reference basis for subsequent research on spaceborne GNSS-R sea surface altimetry.
基金supported by the National Natural Science Foundation of China(No.62173107).
摘要Spaceborne antennas are essential for remote sensing,deep-space communication,and Earth observation,yet their trajectory planning is complicated by nonlinear base-manipulator coupling and antenna flexibility.To address these challenges,this paper proposes a multi-objective trajectory optimization framework.The system dynamics capture both nonlinear rigid-flexible coupling and antenna deformation through a reduced-order formulation.To enhance discretization efficiency,a predictive-terminal hp-adaptive pseudospectral method is employed,assigning collocation density based on task-phase characteristics:finer resolution is applied to dynamic segments requiring higher accuracy,especially near the terminal phase.This enables efficient transcription of the continuous-time problem into a Nonlinear Programming Problem(NLP).The resulting NLP is then solved using a multi-objective optimization strategy based on the nondominated sorting genetic algorithm II,which explores trade-offs among antenna pointing accuracy,energy consumption,and structural vibration.Numerical results demonstrate that the proposed method achieves a reduction of approximately 14.0% in control energy and 41.8%in peak actuation compared to a GPOPS-II baseline,while significantly enhancing vibration suppression.The resulting Pareto front reveals structured trade-offs and clustered solutions,offering robust and diverse options for precision,low-disturbance mission planning.
基金supported by the National Science Fund for Distinguished Young Scholars(62325104).
摘要The spaceborne inverse synthetic aperture radar(ISAR)has attracted significant attention due to its extensive observation range and imaging performance.However,the complex motion between spaceborne platform and the air target leads to complex signal modulation.The scattering anisotropy and occlusion lead to the scattering points missing problem.These problems pose a serious challenge to the traditional ISAR imaging algorithms.Aiming at the above problems,this paper proposes a joint intra-frame and inter-frame imaging algorithm based on spaceborne ISAR.In this algorithm,we divide the long coherent processing interval into several sub-apertures using the narrow-band tracking data,and each-order terms of the signal within sub-aperture is derived in detail.Then,the intra-frame algorithm based on the parametric minimized image entropy search is proposed to correct the spatial-variant phase errors caused by the complex relative motion.As to the well-focused images from different views and the rotation parameters obtained in sub-apertures,the inter-frame algorithm based on wavelet transform can perform image registration and image fusion to obtain more detailed target feature information and more complete target structure.In simulated and real-measured data experiments,the effectiveness and superiority of the proposed algorithm are validated.
基金supported by the National Key Research and Development Program of China(Nos.SQ2022YFB3900026 and 2022YFB3903305)supported by the Leading Talents of Guangdong Pearl River Talent Program(No.2021CX02S024)the Guangdong S&T programme(No.2024B1212050011).
摘要Accurate digital terrain models(DTMs)are essential for a wide range of geospatial and environmental applications,yet their derivation in forested regions remains a significant challenge.Existing global DTMs,typically generated from satellite stereo photogrammetry or interferometric synthetic aperture radar(InSAR),fail to accurately capture understory terrain due to limited penetration capabilities,resulting in elevation overestimation in densely vegetated areas.While airborne light detection and ranging(LiDAR)can provide high-accuracy DTMs,its limited spatial coverage and high acquisition cost hinder large-scale applications.Thus,there is an urgent need for a scalable and cost-effective approach to extract DTMs directly from satellite-derived digital surface models(DSMs).In this study,we propose a simple,interpretable understory terrain extraction method that utilizes canopy height data from Global Ecosystem Dynamics Investigation(GEDI)and Ice,Cloud,and Land Elevation Satellite-2(ICESat-2)to construct a tree height surface model,which is then subtracted from the stereo-derived DSM to generate the final DTM.By directly incorporating LiDAR constraints,the method avoids error propagation from multiple heterogeneous datasets and reduces reliance on ancillary inputs,ensuring ease of implementation and broad applicability.In contrast to machine learning-based terrain modeling methods,which are often prone to overfitting and data bias,the proposed approach is simple,interpretable,and robust across diverse forested landscapes.The accuracy of the resulting DTM was validated against airborne LiDAR reference data and compared with both the Copernicus Digital Elevation Model(DEM)and the forest and buildings removed DEM(FABDEM),a global bare-earth elevation model corrected for vegetation bias.The results indicate that the proposed DTM consistently outperforms the Copernicus DEM(CopDEM)and achieves accuracy comparable to FABDEM.In addition,its finer spatial resolution of 1 m,compared to the 30 m resolution of FABDEM,allows for more detailed terrain representation and better capture of fine-scale variation.This advantage is most pronounced in gently to moderately sloped areas,where the proposed DTM shows clearly higher accuracy than both the CopDEM and FABDEM.The results confirm that high-resolution DTMs can be effectively extracted from DSMs using spaceborne LiDAR constraints,offering a scalable solution for terrain modeling in forested environments where airborne LiDAR is unavailable.To illustrate the potential utility of the proposed DTM,we applied it to a fire risk mapping application based on topographic parameters such as slope,aspect,and elevation.This case highlights how improved terrain representation can support geospatial hazard assessments.
基金supported by Natural Science and Technology Planning Foundation of Guangxi (guikeAD23026257)the National Natural Science Foundation of China (42064002 and 42074029)and the“Ba Gui Scholars”program of the provincial government of Guangxi。
摘要Spaceborne global navigation satellite system-reflectometry has become an effective technique for Soil Moisture(SM)retrieval.However,the accuracy of global SM retrieval using a single model is limited due to the complexity of land surface.Introducing redundant ancillary data may also result in over-reliance problems.Therefore,we propose a method for SM retrieval that considers geographical disparities using the data from Cyclone GNSS(CYGNSS)obser-vations and Soil Moisture Active and Passive(SMAP)product.Based on the CYGNSS effective reflectivity and ancillary datasets of SMAP,we establish five models for each grid with different parameters to achieve global SM retrieval.Subsequently,an optimal model,determined by the performance indicator,is used for SM retrieval.The results show that the root mean square error SRMsE with the improved methodis decreased by 9.1%using SMAP SM as reference with the SRMsE=0.040 cm3/cm3 compared with using single reflectivity-temperature-vegetation method.Additionally,using the in-situ SM of International Soil Moisture Network as reference,the overall correlation coeffcient R and SRMSE values with the improved method are 0.80 and 0.064 cm3/cm3,respectively.The average R of the chosen sites is increased by 22.7%,and the average SRMse is decreased by 8.7%.The results indicate that the improved method can better retrieve SM in both global and local scales without redundant auxiliary data.
基金The Shandong Key Laboratory of Marine Ecological Environment and Disaster Prevention and Mitigation under contract No.202408the Key Program of Joint Fund of the National Natural Science Foundation of China and Shandong Province under contract No.U22A20586the National Natural Science Foundation of China under contract No.42274159。
摘要Global Navigation Satellite System Reflectometry(GNSS-R)remote sensing has demonstrated broad application potential in marine oil spill monitoring due to its advantages such as all-weather capability,wide spatial coverage,and high spatiotemporal resolution.However,the morphological features of Delay-Doppler Map(DDM)provided by GNSSR are simultaneously influenced by factors such as wind speed and surface oil films,leading to high false positive rates in traditional oil spill detection methods.To address this challenge,this study proposes a multimodal recognition framework that integrates both DDM imagery and wind speed information.A dual-branch Residual Network(Res Net)-based feature extraction network is designed to extract high-dimensional features from DDM and wind speed data,which are then fused at the feature level to enable accurate identification of oil-contaminated areas.To alleviate the scarcity of labeled real-world samples,a large-scale synthetic multimodal dataset is generated based on the ZavorotnyVoronovich(Z-V)scattering model for pretraining,followed by transfer learning using a small number of real GNSS-R observations to enhance the model's adaptability to real marine environments.Experimental results show that the proposed method achieves 91%classification accuracy on the test set,significantly outperforming baseline models such as Convolutional Neural Networks(CNNs).Furthermore,the incorporation of wind speed as an auxiliary modality effectively reduces false positives caused by wind-wave interference and enhances the model's sensitivity to oil film signatures.This study provides a feasible theoretical foundation and technical pathway for the application of GNSS-R in marine oil spill detection.