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Mixed-state Berry curvature in quantum multiparameter estimations 认领 引用
1
作者 Xiaoguang Wang Xiao-Ming Lu +2 位作者 Yunbo Zhang Libin Fu Shu Chen 《Communications in Theoretical Physics》 SCIE CAS CSCD 2026年第5期80-84,共5页
For pure states,the quantum Berry curvature has been well studied.However,the quantum curvature for mixed states has received less attention.From the concept of symmetric logarithmic derivative,we introduce a mixed-st... For pure states,the quantum Berry curvature has been well studied.However,the quantum curvature for mixed states has received less attention.From the concept of symmetric logarithmic derivative,we introduce a mixed-state quantum curvature and find that it plays a key role in the field of multi-parameter precision estimations.Through spectral decomposition,we derive the mixed-state Berry curvature for both the full-rank and non-full-rank density matrices.As an example,we obtain the exact expression of the Berry curvature for an arbitrary qubit state. 展开更多
关键词 Berry curvature Uhlmann curvature mixed states quantum parameter estimation
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Off-Grid Parameter Estimation for OFDM-Based ISAC Systems with Incomplete Data and Demodulation Errors 认领 引用
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作者 Feng Xinxin Wu Weilin +1 位作者 Ling Muyao Zheng Haifeng 《China Communications》 SCIE EI CSCD 2026年第3期199-216,共18页
In the 6G environment,addressing challenges like missing data,demodulation errors,and offgrid issues during target parameter estimation is a significant hurdle for integrated sensing and communication(ISAC)systems.In ... In the 6G environment,addressing challenges like missing data,demodulation errors,and offgrid issues during target parameter estimation is a significant hurdle for integrated sensing and communication(ISAC)systems.In the ISAC framework,a commonly used method for parameter estimation is compressive sensing.However,it often struggles with off-grid problems in continuous parameter estimation.In contrast,the atomic norm has been proven effective in overcoming these off-grid issues,making it a more suitable approach for continuous parameter estimation.In this paper,we investigate the application of atomic norm in ISAC and propose an ISAC model based on orthogonal frequency division multiplexing(OFDM)for parameter estimation.We utilize the atomic norm under conditions of incomplete data and demodulation errors.To enhance the convergence speed and accuracy of our algorithm,we implement the alternating direction method of multipliers(ADMM)for iterative processing.We refer to this algorithm as ANMI.Building on this foundation,we develop a deep unfolding network algorithm,ANMIADMM-Net,which further mitigates the impact of missing data and demodulation errors on target parameter estimation by training optimal parameters.Experimental results demonstrate that our proposed ANMI and ANMI-ADMM-Net accurately estimate target parameters even in the presence of missing data and demodulation errors,exhibiting superior precision and robustness compared to traditional methods. 展开更多
关键词 ADMM atomic norm deep unfolding networks ISAC off-grid target parameter estimation
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UAV-to-Ground Channel Modeling:(Quasi-)Closed-Form Channel Statistics and Manual Parameter Estimation 认领 引用 被引量：1
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作者 Zeng Linzhou Liao Xuewen +3 位作者 Xie Wenwu Ma Zhangfeng Xiong Baiping Jiang Hao 《China Communications》 SCIE EI CSCD 2026年第1期47-66,共20页
(Quasi-)closed-form results for the statistical properties of unmanned aerial vehicle(UAV)airto-ground channels are derived for the first time using a novel spatial-vector-based method from a threedimensional(3-D)arbi... (Quasi-)closed-form results for the statistical properties of unmanned aerial vehicle(UAV)airto-ground channels are derived for the first time using a novel spatial-vector-based method from a threedimensional(3-D)arbitrary-elevation one-cylinder model.The derived results include a closed-form expression for the space-time correlation function and some quasi-closed-form ones for the space-Doppler power spectrum density,the level crossing rate,and the average fading duration,which are shown to be the generalizations of those previously obtained from the two-dimensional(2-D)one-ring model and the 3-D low-elevation one-cylinder model for terrestrial mobile-to-mobile channels.The close agreements between the theoretical results and the simulations as well as the measurements validate the utility of the derived channel statistics.Based on the derived expressions,the impacts of some parameters on the channel characteristics are investigated in an effective,efficient,and explicable way,which leads to a general guideline on the manual parameter estimation from the measurement description. 展开更多
关键词 channel characteristics geometry-based stochastic model manual parameter estimation UAV channel modeling
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Variational parameter estimation in a two-equation turbulence model:assimilation in a realistic context using satellite and in situ observations 认领 引用
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作者 Yan Hu Wei Li +1 位作者 Lianxin Zhang Xuefeng Zhang 《Acta Oceanologica Sinica》 SCIE CAS CSCD 2026年第4期213-224,共12页
This study presents a four-dimensional variational(4D-Var)data assimilation framework to optimize two uncertain wave-induced parameters(wave energy factor α and Charnock coefficient β)within the Mellor-Yamada level ... This study presents a four-dimensional variational(4D-Var)data assimilation framework to optimize two uncertain wave-induced parameters(wave energy factor α and Charnock coefficient β)within the Mellor-Yamada level 2.5(MY-2.5)turbulence closure scheme.The effectiveness of the optimization is then evaluated by its impact on improving sea surface temperature(SST)forecasts.This study represents one of the first attempts to estimate higher-order turbulent parameters using real observational data.As the extension of prior twin experiments,observational data are sourced from a fusion of satellite and in situ measurements.Extending previous twin experiments,the observational dataset combines satellite and in situ measurements,effectively mitigating the limitations of satellite accuracy and the sparse spatial coverage of in situ observations.In real-data assimilation experiments,joint optimization of the initial field and the two parameters yielded limited parameter improvement.This is attributed to the lower sensitivity of the two parameters compared to the initial field within the short assimilation time window.However,optimizing the parameters after the initial field reaches a quasi-balanced state further enhances SST prediction accuracy.Quantitatively,the SST forecast root-mean-square error is reduced by approximately 22%compared to simulations without parameter optimization.Crucially,the practical value of parameter optimization lies not in determining true parameter values,but in its capacity to counteract inherent systematic model errors,such as model bias and resolution constraints.While the spatial invariance of parameters imposes inherent limitations on forecast performance,the experiments demonstrate that optimizing α and β via 4D-Var yields measurable benefits for SST prediction accuracy. 展开更多
关键词 parameter estimation 4D-Var wave breaking MY-2.5 turbulence closure
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Photovoltaic Parameter Estimation Using a Parallelized Triangulation Topology Aggregation Optimization with Real-World Dataset Validation 认领 引用
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作者 Jun Zhe Tan Rodney H.G.Tan +4 位作者 Nor Ashidi Mat Isa Sew Sun Tiang Chun Kit Ang Kuo-Ping Lin Wei Hong Lim 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第2期691-736,共46页
Accurate estimation of photovoltaic(PV)parameters is essential for optimizing solar module perfor-mance and enhancing resource efficiency in renewable energy systems.This study presents a process innovation by introdu... Accurate estimation of photovoltaic(PV)parameters is essential for optimizing solar module perfor-mance and enhancing resource efficiency in renewable energy systems.This study presents a process innovation by introducing,for the first time,the Triangulation Topology Aggregation Optimizer(TTAO)integrated with parallel computing to address PV parameter estimation challenges.The effectiveness and robustness of TTAO are rigorously evaluated using two standard benchmark datasets(KC200GT and R.T.C.France solar cells)and a real-world dataset(Poly70W solar module)under single-,double-,and triple-diode configurations.Results show that TTAO consistently achieves superior accuracy by producing the lowest RMSE values and faster convergence compared to state-of-the-art metaheuristic algorithms.In addition,the integration of parallel computing significantly enhances computational efficiency,reducing execution time by up to 85%without compromising accuracy.Validation using real-world data further demonstrates TTAO’s adaptability and practical relevance in renewable energy systems,effectively bridging the gap between theoretical modeling and real-world implementation for PV system monitoring and optimization,contributing to climate mitigation through improved solar energy performance. 展开更多
关键词 Photovoltaic(PV) parameters estimation triangulation topology aggregation optimizer(TTAO) parallel computing optimization
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ASYMPTOTIC BEHAVIOR AND PARAMETER ESTIMATION OF A WEIGHTED SELF-REPELLING DIFFUSION DRIVEN BYα-STABLE MOTION 认领 引用
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作者 Yinmeng CHEN Xiaoyu XIA Litan YAN 《Acta Mathematica Scientia》 SCIE CSCD 2026年第4期1945-1956,共12页
Let Mα={Mtα,t≥0}be anα-stable motion on R with 00.Whenθ>0 and 1/β+1<a<2,we show that the convergence Jtα(0,θ,β):=(1+t)βe-G(t)Xtα→ξ∞α+ν/θand Jtα(n,θ,β... Let Mα={Mtα,t≥0}be anα-stable motion on R with 00.Whenθ>0 and 1/β+1<a<2,we show that the convergence Jtα(0,θ,β):=(1+t)βe-G(t)Xtα→ξ∞α+ν/θand Jtα(n,θ,β):=θ(1+t)β+1(Jtα(n-1,θ,β)λn-1(ξ∞α+ν/θ))→λn(t→∞)hold almost surely for all n≥1,where G(t)=∫0tθ(1+s)βds,ξ∞α=∫0∞(1+s)βe-G(s)d Msαandλn=β(2β+1)...(nβ+n-1).Using least squares method,we study estimators ofθandνand give their asymptotic distributions. 展开更多
关键词 asymptotic behavior least squares method stable motion weighted self repelling diffusion parameter estimation large time behaviors
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Advanced Meta-Heuristic Optimization for Accurate Photovoltaic Model Parameterization:A High-Accuracy Estimation Using Spider Wasp Optimization 认领 引用
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作者 Sarah M.Alhammad Diaa Salama AbdElminaam +1 位作者 Asmaa Rizk Ibrahim Ahmed Taha 《Computers, Materials & Continua》 SCIE EI 2026年第3期2269-2303,共35页
Accurate parameter extraction of photovoltaic(PV)models plays a critical role in enabling precise performance prediction,optimal system sizing,and effective operational control under diverse environmental conditions.W... Accurate parameter extraction of photovoltaic(PV)models plays a critical role in enabling precise performance prediction,optimal system sizing,and effective operational control under diverse environmental conditions.While a wide range of metaheuristic optimisation techniques have been applied to this problem,many existing methods are hindered by slow convergence rates,susceptibility to premature stagnation,and reduced accuracy when applied to complex multi-diode PV configurations.These limitations can lead to suboptimal modelling,reducing the efficiency of PV system design and operation.In this work,we propose an enhanced hybrid optimisation approach,the modified Spider Wasp Optimization(mSWO)with Opposition-Based Learning algorithm,which integrates the exploration and exploitation capabilities of the Spider Wasp Optimization(SWO)metaheuristic with the diversityenhancing mechanism of Opposition-Based Learning(OBL).The hybridisation is designed to dynamically expand the search space coverage,avoid premature convergence,and improve both convergence speed and precision in highdimensional optimisation tasks.The mSWO algorithm is applied to three well-established PV configurations:the single diode model(SDM),the double diode model(DDM),and the triple diode model(TDM).Real experimental current-voltage(I-V)datasets from a commercial PV module under standard test conditions(STC)are used for evaluation.Comparative analysis is conducted against eighteen advanced metaheuristic algorithms,including BSDE,RLGBO,GWOCS,MFO,EO,TSA,and SCA.Performance metrics include minimum,mean,and maximum root mean square error(RMSE),standard deviation(SD),and convergence behaviour over 30 independent runs.The results reveal that mSWO consistently delivers superior accuracy and robustness across all PV models,achieving the lowest RMSE values of 0.000986022(SDM),0.000982884(DDM),and 0.000982529(TDM),with minimal SD values,indicating remarkable repeatability.Convergence analyses further show that mSWO reaches optimal solutions more rapidly and with fewer oscillations than all competing methods,with the performance gap widening as model complexity increases.These findings demonstrate that mSWO provides a scalable,computationally efficient,and highly reliable framework for PV parameter extraction.Its adaptability to models of growing complexity suggests strong potential for broader applications in renewable energy systems,including performance monitoring,fault detection,and intelligent control,thereby contributing to the optimisation of next-generation solar energy solutions. 展开更多
关键词 modified Spider Wasp Optimizer(mSWO) photovoltaic(PV)modeling meta-heuristic optimization solar energy parameter estimation renewable energy technologies
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V2X-Enabled Parameter-Estimation-Based ILC for Repetitive Trajectory Tracking of Connected Vehicles under Trial-Varying Conditions 认领 引用
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作者 Ping Ma Quan Wang Yiyang Chen 《Computers, Materials & Continua》 SCIE EI 2026年第10期1589-1610,共22页
Connected vehicles operating in V2X-enabled intelligent transportation systems often perform repetitive trajectory tracking in repeated tasks.In practical applications,traffic conditions,communication quality,and sens... Connected vehicles operating in V2X-enabled intelligent transportation systems often perform repetitive trajectory tracking in repeated tasks.In practical applications,traffic conditions,communication quality,and sensing accuracy may vary from trial to trial.These variations induce time-varying dynamics across repeated runs and reduce the effectiveness of iterative learning control(ILC)schemes when fixed or inaccurately identified models are used.To address this issue,this paper proposes a parameter-estimation-based ILC framework for connected vehicles.Parameter estimation is integrated with a norm-optimal ILC design through an expectation-maximization strategy.The time-varying model parameters and the learning input are updated iteratively.By exploiting the estimated parameter information at each trial,the proposed method improves tracking performance under random noise and trial-varying operating conditions.Convergence properties are analyzed for both noise-free and noisy cases.Comparative results demonstrate improved tracking accuracy and convergence performance over several benchmark methods. 展开更多
关键词 Repetitive trajectory tracking iterative learning control parameter estimation
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Radar Micro-Doppler Parameter Estimation Method Based on Gated Dual-Path Dynamic-Wavelet Convolutional Network 认领 引用
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作者 Jinhao Wang Xiaolong Chen +2 位作者 Xinghai Wang Jian Guan Xiaolin Du 《Journal of Beijing Institute of Technology》 EI CAS 2025年第6期566-576,共11页
Micro-Doppler parameter estimation is crucial for moving targets.However,conventional methods face limitations like inadequate time-frequency(TF)resolution and poor generalization,while existing deep learning approach... Micro-Doppler parameter estimation is crucial for moving targets.However,conventional methods face limitations like inadequate time-frequency(TF)resolution and poor generalization,while existing deep learning approaches often treat TF analysis as a fixed preprocessing step.To overcome these challenges,this paper introduces a radar micro-Doppler parameter estimation method based on a gated dual-path dynamic-wavelet convolutional network(GDWCN).The GDWCN is an end-to-end deep learning framework that maps raw radar signals to micro-motion parameters by integrating clutter suppression,gated dual-path module,feature extraction,and parameter regression.Its core innovation is a gated dual-path module that combines dynamic convolution and learnable wavelet convolution,selecting the optimal processing path based on input signal characteristics.For the Inspire 2 drone,GDWCN reduced the mean absolute error(MAE)of frequency estimation by approximately 38%compared to the enhanced time-frequency micro-Doppler network,and its relative error by approximately 69%compared to the short-time Fourier transform(STFT),and 58%over the local maximum synchroextracting transform.Ablation studies further confirm the efficacy of the clutter suppression module and the attention mechanism. 展开更多
关键词 parameter estimation radar signal processing deep learning time-frequency analysis
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Estimation of truck mass and center of gravity using data-driven mechanics:a comparative study of TSO-SVM,CS-BP,SSA-ELM,and WOA-XGBoost in automotive dynamics 认领 引用
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作者 Wei Liu Yuanyou Fu +2 位作者 Yongjun Pan Weicheng Huang Aki Mikkola 《Acta Mechanica Sinica》 SCIE EI CAS CSCD 2026年第2期160-177,共18页
Accurate estimation of a truck’s mass and center of gravity(CG)is critical for optimizing safety and performance butremains challenging due to dynamic uncertainties in weight distribution and road interactions.This s... Accurate estimation of a truck’s mass and center of gravity(CG)is critical for optimizing safety and performance butremains challenging due to dynamic uncertainties in weight distribution and road interactions.This study introduces a datadriven mechanics framework integrating four hybrid machine learning(ML)models-tuna search-optimized support vector machine,cuckoo search-optimized BP neural networks,sparrow search algorithm-optimized extreme learning machine,and whalesearch-optimized XGBoost-to enable estimation.A 17-degree-of-freedom multibody dynamics model,incorporating suspension kinematics via a semirecursive formulation,generates simulation datasets linking real-time tuck states(pitch,roll)to massand CG.Search algorithms leverage physics-derived truck state data to initialize ML hyperparameters,enhancing training efficiency.Validation against multibody benchmarks confirms accuracy,while robustness is demonstrated across driving scenariosand noise.By unifying data-driven ML with physics-based mechanics,this approach advances parameter estimation,bridgingtruck dynamics with computational intelligence for automotive design. 展开更多
关键词 Parameter estimation Data-driven mechanics Multibody dynamics Center of gravity Hybrid machine learning Real-time estimation
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Online Estimation of DC-link Capacitor Parameters of Three-Level NPC Converters Using Inherent Signals Analysis 认领 引用 被引量：1
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作者 Ricardo Lucio de Araujo Ribeiro Reuben Palmer Rezende de Sousa +2 位作者 Alexandre Cunha Oliveira Antonio Marcus Nogueira Lima Qing-Long Han 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2025年第7期1434-1444,共11页
This paper presents a method for estimating the parameters of DC-link capacitors in three-level NPC voltage source inverters(3L-NPC-VSI)used in grid-tied systems.The technique uses the signals generated by the intermo... This paper presents a method for estimating the parameters of DC-link capacitors in three-level NPC voltage source inverters(3L-NPC-VSI)used in grid-tied systems.The technique uses the signals generated by the intermodulation caused by the PWM strategy and converter topology interaction to estimate the capacitor parameters of the converter DC-link.It utilizes an observer-based structure consisting of a recursive noninteger sliding discrete Fourier transform(rnSDFT)and an RLS filter improved with a forgetting factor(oSDFT-RLS)to accurately estimate the capacitance and equivalent series resistance(ESR).Importantly,this method does not require additional sensors beyond those already installed in off-the-shelf 3L-NPC-VSI systems,ensuring its noninvasiveness.Furthermore,the oSDFTRLS estimates capacitor parameters in the time-frequency domain,enabling the tracking of capacitor degradation and predicting potential faults.Experimental results from the laboratory setup demonstrate the effectiveness of the proposed condition monitoring method. 展开更多
关键词 Aluminum electrolytic capacitors(AEC) condition monitoring forgetting factor inherent signals parameter estimation recursive least squares(RLS) sliding discrete Fourier transform(SDFT) three-level NPC converter
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Sensitivity-based state and parameter moving horizon estimation method for liquid propellant rocket engine 认领 引用
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作者 Zizhao WANG Dan WANG +2 位作者 Hongyu CHEN Zhijiang SHAO Zhengyu SONG 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2025年第7期46-60,共15页
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
关键词 Sensitivity Moving horizon estimation Noise covariance estimation Parameter estimation Liquid propellant rocket engine
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Analysis and estimation of wave-induced Doppler shift from low-incidence-angle RAR based on sea state parameters 认领 引用
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作者 Jing Ye Yong Wan +3 位作者 Chenqing Fan Yongshou Dai Yisen Yang Xiangying Miao 《Acta Oceanologica Sinica》 SCIE CAS CSCD 2025年第9期169-182,共14页
The research on ocean dynamics information plays a crucial role in understanding ocean phenomena, assessing marine environmental impacts, and guiding engineering designs. The Doppler information observed by radars ref... The research on ocean dynamics information plays a crucial role in understanding ocean phenomena, assessing marine environmental impacts, and guiding engineering designs. The Doppler information observed by radars reflects sea surface dynamics, to which ocean waves make important contributions. Low-incidence-angle real aperture radar(RAR)demonstrates great potential for independently observing vectorial Doppler information on the ocean surface. To systematically characterize and accurately estimate the wave-induced Doppler frequency shift(WVF) from lowincidence-angle RAR, this study conducts comprehensive influencing factor analysis and establishes sea-stateparameterized WVF models. First, a simulated WVF dataset is generated under a rotating low-incidence-angle RAR.The feature parameters of WVF are then determined by analysing contributing factors including wind waves, swells,and sea state parameters. Furthermore, two WVF models(WVF_Ku P9 with 9 inputs and WVF_Ku P4 with 4 inputs) are constructed by the Transformer encoder for different application scenarios. Both models achieve high accuracy for WVF estimation with root mean square errors(RMSE) of 1.874 Hz and 2.716 Hz, respectively. The reliability and superiority of the proposed models are validated through comparisons with the Ka DOP, which is a typical geophysical model function(GMF). The findings in this paper advance the understanding of WVF characteristics and generation mechanisms. The proposed estimation models can provide reliable estimates, offering critical references for lowincidence-angle RAR applications such as ocean surface current retrieval. 展开更多
关键词 wave-induced Doppler shift parameter estimation low-incidence-angle real aperture radar sea state parameters Transformer encoder
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Online Estimation Method of Train Wheel-Rail Adhesion Coefficient Based on Parameter Estimation 认领 引用
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作者 Yi Zhang Wenliang Zhu +2 位作者 Hanbin Wang Chun Tian Jiajun Zhou 《Computer Modeling in Engineering & Sciences》 SCIE EI 2025年第9期2873-2891,共19页
Aiming to address the challenge of directly measuring the real-time adhesion coefficient between wheels and rails,this paper proposes an online estimation algorithm for the adhesion coefficient based on parameter esti... Aiming to address the challenge of directly measuring the real-time adhesion coefficient between wheels and rails,this paper proposes an online estimation algorithm for the adhesion coefficient based on parameter estimation.Firstly,a force analysis of the single-wheel pair model of the train is conducted to derive the calculation relationship for the wheel-rail adhesion coefficient in train dynamics.Then,an estimator based on parameter estimation is designed,and its stability is verified.This estimator is combined with the wheelset force analysis to estimate the wheel-rail adhesion coefficient.Finally,the approach is validated through joint simulations on the MATLAB/Simulink and AMESim platforms,as well as a hardware-in-the-loop semi-physical simulation experimental platform that accounts for system delay and noise conditions.The results indicate that the proposed algorithm effectively tracks changes in the adhesion coefficient during train braking,including the decrease in adhesion when the train brakes and slides,and the overall increase as the train speed decreases.The effectiveness of the algorithm was verified by setting different test conditions.The results show that the estimation algorithm can accurately estimate the adhesion coefficient,and through error analysis,it is found that the error between the estimated value of the adhesion coefficient and the theoretical value of the adhesion coefficient is within 5%.The adhesion coefficient obtained through the online estimation method based on the parameter estimation proposed in this paper demonstrates strong followability in both simulation and practical applications. 展开更多
关键词 Hardware-in-the-loop simulation online parameter estimation rail vehicle wheel-rail adhesion
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Parameters Estimation of Modified Triple Diode Model of PSCs Considering Charge Accumulations and Electric Field Effects Using Puma Optimizer 认领 引用
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作者 Amlak Abaza Ragab A.El-Sehiemy +1 位作者 Mona Gafar Ahmed Bayoumi 《Computer Modeling in Engineering & Sciences》 SCIE EI 2025年第4期723-745,共23页
Promoting the high penetration of renewable energies like photovoltaic(PV)systems has become an urgent issue for expanding modern power grids and has accomplished several challenges compared to existing distribution g... Promoting the high penetration of renewable energies like photovoltaic(PV)systems has become an urgent issue for expanding modern power grids and has accomplished several challenges compared to existing distribution grids.This study measures the effectiveness of the Puma optimizer(PO)algorithm in parameter estimation of PSC(perovskite solar cells)dynamic models with hysteresis consideration considering the electric field effects on operation.The models used in this study will incorporate hysteresis effects to capture the time-dependent behavior of PSCs accurately.The PO optimizes the proposed modified triple diode model(TDM)with a variable voltage capacitor and resistances(VVCARs)considering the hysteresis behavior.The suggested PO algorithm contrasts with other wellknown optimizers from the literature to demonstrate its superiority.The results emphasize that the PO realizes a lower RMSE(Root mean square errors),which proves its capability and efficacy in parameter extraction for the models.The statistical results emphasize the efficiency and supremacy of the proposed PO compared to the other well-known competing optimizers.The convergence rates show good,fast,and stable convergence rates with lower RMSE via PO compared to the other five competitive optimizers.Moreover,the lowermean realized via the PO optimizer is illustrated by the box plot for all optimizers. 展开更多
关键词 Dynamic model of PSCs puma optimizer parameter estimation triple diode model
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High Resolution Sparse Imaging and Doppler Parameter Estimation of Fucheng⁃1 SAR Satellite 认领 引用
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作者 CAI Fuxuan ZHU Ziyi +1 位作者 SONG Yufan BI Hui 《Transactions of Nanjing University of Aeronautics and Astronautics》 EI CSCD 2025年第4期487-496,共10页
In recent years,the development of domestic commercial synthetic aperture radar(SAR)is in full swing,with multiple commercial SAR satellites in orbit,showing great potential in disaster monitoring,natural resource man... In recent years,the development of domestic commercial synthetic aperture radar(SAR)is in full swing,with multiple commercial SAR satellites in orbit,showing great potential in disaster monitoring,natural resource management and deformation observation.Fucheng-1 is the first C-band commercial SAR satellite for interferometric SAR(InSAR)service developed by Spacety China,which marks the gradual maturity of China’s remote sensing data service.Based on the raw data collected by Fucheng-1,this paper firstly introduces the range-Doppler algorithm(RDA),then illustrates the parameter estimation method on the basis of fractional Fourier transform(FrFT)to realize the accurate estimation of azimuth chirp rate,which effectively improves imaging quality.Finally,the L1-norm regularization based sparse imaging method is utilized to reconstruct images from down-sampled data.Experimental results show that the sparse imaging algorithm can accurately reconstruct the down-sampled Fucheng-1 data and suppress sidelobes and clutter. 展开更多
关键词 Fucheng-1 synthetic aperture radar(SAR) sparse SAR imaging parameter estimation
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Revisiting the quantum parameter estimation approach for Mach–Zehnder interferometry 认领 引用
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作者 Bing-Shu Hu Xiao-Ming Lu 《Communications in Theoretical Physics》 SCIE CAS CSCD 2025年第10期132-137,共6页
The Mach-Zehnder interferometer is a fundamental tool for measuring phase shifts between two light paths,serving as a crucial prototype for achieving high-precision measurements in various scientific and technological... The Mach-Zehnder interferometer is a fundamental tool for measuring phase shifts between two light paths,serving as a crucial prototype for achieving high-precision measurements in various scientific and technological applications.In this study,we analyze different models for estimating the relative phase shift in a general two-arm Mach-Zehnder interferometer.We demonstrated that single-parameter estimation models can be reduced from the two-parameter estimation model by imposing appropriate constraints on the parameter space.To make quantum Fisher information of the single-parameter estimation models meaningful,the corresponding constraints must be guaranteed in the experiment implementation.Furthermore,we apply the quantum Fisher information approach to analyze the Mach-Zehnder interferometer with the an input state composed of a displaced squeezed vacuum state and a coherent state,providing insights into the precision limits of such configurations. 展开更多
关键词 quantum parameter estimation Mach-Zehnder interferometry quantum metrology
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Incoherence parameter estimation and multiband fusion based on the novel structure-enhanced spatial spectrum algorithm 认领 引用
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作者 JIANG Libing ZHENG Shuyu +2 位作者 YANG Qingwei ZHANG Xiaokuan WANG Zhuang 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2025年第4期867-879,共13页
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
关键词 multiband fusion incoherence parameter estimation matrix pencil(MP) root-multiple signal classification(Root-MUSIC) covariance matrix.
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Aerodynamic Modeling and Parameter Estimation from QAR Data of an Airplane Approaching a High-altitude Airport 认领 引用 被引量：23
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作者 WANG Qing WU Kaiyuan +2 位作者 ZHANG Tianjiao KONG Yi'nan QIAN Weiqi 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2012年第3期361-371,共11页
Aerodynamic modeling and parameter estimation from quick accesses recorder （QAR） data is an important technical way to analyze the effects of highland weather conditions upon aerodynamic characteristics of airplane.... Aerodynamic modeling and parameter estimation from quick accesses recorder （QAR） data is an important technical way to analyze the effects of highland weather conditions upon aerodynamic characteristics of airplane. It is also an essential content of flight accident analysis. The related techniques are developed in the present paper, including the geometric method for angle of attack and sideslip angle estimation, the extended Kalman filter associated with modified Bryson-Frazier smoother （EKF-MBF） method for aerodynamic coefficient identification, the radial basis function （RBF） neural network method for aerodynamic mod- eling, and the Delta method for stability/control derivative estimation. As an application example, the QAR data of a civil air- plane approaching a high-altitude airport are processed and the aerodynamic coefficient and derivative estimates are obtained. The estimation results are reasonable, which shows that the developed techniques are feasible. The causes for the distribution of aerodynamic derivative estimates are analyzed. Accordingly, several measures to improve estimation accuracy are put forward. 展开更多
关键词 civil airplane aerodynamics QAR data aerodynamic modeling aerodynamic parameter estimation flight safety EKF-MBF method neural network
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Research on LPI radar signal detection and parameter estimation technology 认领 引用 被引量：9
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作者 WAN Tao JIANG Kaili +2 位作者 LIAO Jingyi JIA Tingting TANG Bin 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2021年第3期566-572,共7页
Modern radar signals mostly use low probability of intercept(LPI)waveforms,which have short pulses in the time domain,multicomponent properties,frequency hopping,combined modulation waveforms and other characteristics... Modern radar signals mostly use low probability of intercept(LPI)waveforms,which have short pulses in the time domain,multicomponent properties,frequency hopping,combined modulation waveforms and other characteristics,making the detection and estimation of LPI radar signals extremely difficult,and leading to highly required significant research on perception technology in the battlefield environment.This paper proposes a visibility graphs(VG)-based multicomponent signals detection method and a modulation waveforms parameter estimation algorithm based on the time-frequency representation(TFR).On the one hand,the frequency domain VG is used to set the dynamic threshold for detecting the multicomponent LPI radar waveforms.On the other hand,the signal is projected into the time and frequency domains by the TFR method for estimating its symbol width and instantaneous frequency(IF).Simulation performance shows that,compared with the most advanced methods,the algorithm proposed in this paper has a valuable advantage.Meanwhile,the calculation cost of the algorithm is quite low,and it is achievable in the future battlefield. 展开更多
关键词 multicomponent signals detection parameter estimation visibility graphs(VG) low probability of intercept(LPI) time-frequency representation(TFR)
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