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
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
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 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
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
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
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
In this paper, based on the theory of parameter estimation, we give a selection method and, in a sense of a good character of the parameter estimation, we think that it is very reasonable. Moreover, we offer a calcula...In this paper, based on the theory of parameter estimation, we give a selection method and, in a sense of a good character of the parameter estimation, we think that it is very reasonable. Moreover, we offer a calculation method of selection statistic and an applied example.展开更多
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
We study the parameter estimation of a nonlinear chaotic system,which can be essentially formulated as a multidimensional optimization problem.In this paper,an orthogonal learning cuckoo search algorithm is used to es...We study the parameter estimation of a nonlinear chaotic system,which can be essentially formulated as a multidimensional optimization problem.In this paper,an orthogonal learning cuckoo search algorithm is used to estimate the parameters of chaotic systems.This algorithm can combine the stochastic exploration of the cuckoo search and the exploitation capability of the orthogonal learning strategy.Experiments are conducted on the Lorenz system and the Chen system.The proposed algorithm is used to estimate the parameters for these two systems.Simulation results and comparisons demonstrate that the proposed algorithm is better or at least comparable to the particle swarm optimization and the genetic algorithm when considering the quality of the solutions obtained.展开更多
An adaptive unscented Kalman filter(AUKF)and an augmented state method are employed to estimate the timevarying parameters and states of a kind of nonlinear high-speed objects.A strong tracking filter is employed to i...An adaptive unscented Kalman filter(AUKF)and an augmented state method are employed to estimate the timevarying parameters and states of a kind of nonlinear high-speed objects.A strong tracking filter is employed to improve the tracking ability and robustness of unscented Kalman filter(UKF)when the process noise is inaccuracy,and wavelet transform is used to improve the estimate accuracy by the variance of measurement noise.An augmented square-root framework is utilized to improve the numerical stability and accuracy of UKF.Monte Carlo simulations and applications in the rapid trajectory estimation of hypersonic artillery shells confirm the effectiveness of the proposed method.展开更多
Time-frequency-based methods are proven to be effective for parameter estimation of linear frequency modulation （LFM） signals. The smoothed pseudo Winger-Ville distribution （SPWVD） is used for the parameter estima...Time-frequency-based methods are proven to be effective for parameter estimation of linear frequency modulation （LFM） signals. The smoothed pseudo Winger-Ville distribution （SPWVD） is used for the parameter estimation of multi-LFM signals, and a method of the SPWVD binarization by a dynamic threshold based on the Otsu algorithm is proposed. The proposed method is effective in the demand for the estimation of different parameters and the unknown signal-to-noise ratio （SNR） circumstance. The performance of this method is confirmed by numerical simulation.展开更多
By using the sparsity of frequency hopping（FH） signals,an underdetermined blind source separation（UBSS） algorithm is presented. Firstly, the short time Fourier transform（STFT） is performed on the mixed signals. ...By using the sparsity of frequency hopping（FH） signals,an underdetermined blind source separation（UBSS） algorithm is presented. Firstly, the short time Fourier transform（STFT） is performed on the mixed signals. Then, the mixing matrix, hopping frequencies, hopping instants and the hooping rate can be estimated by the K-means clustering algorithm. With the estimated mixing matrix, the directions of arrival（DOA） of source signals can be obtained. Then, the FH signals are sorted and the FH pattern is obtained. Finally, the shortest path algorithm is adopted to recover the time domain signals. Simulation results show that the correlation coefficient between the estimated FH signal and the source signal is above 0.9 when the signal-to-noise ratio（SNR） is higher than 0 d B and hopping parameters of multiple FH signals in the synchronous orthogonal FH network can be accurately estimated and sorted under the underdetermined conditions.展开更多
A retrofitted electro-hydraulic proportional system for hydraulic excavator was introduced firstly. According to the principle and characteristic of load independent flow distribution(LUDV) system,taking boom hydrauli...A retrofitted electro-hydraulic proportional system for hydraulic excavator was introduced firstly. According to the principle and characteristic of load independent flow distribution(LUDV) system,taking boom hydraulic system as an example and ignoring the leakage of hydraulic cylinder and the mass of oil in it,a force equilibrium equation and a continuous equation of hydraulic cylinder were set up. Based on the flow equation of electro-hydraulic proportional valve,the pressure passing through the valve and the difference of pressure were tested and analyzed. The results show that the difference of pressure does not change with load,and it approximates to 2.0 MPa. And then,assume the flow across the valve is directly proportional to spool displacement and is not influenced by load,a simplified model of electro-hydraulic system was put forward. At the same time,by analyzing the structure and load-bearing of boom instrument,and combining moment equivalent equation of manipulator with rotating law,the estimation methods and equations for such parameters as equivalent mass and bearing force of hydraulic cylinder were set up. Finally,the step response of flow of boom cylinder was tested when the electro-hydraulic proportional valve was controlled by the step current. Based on the experiment curve,the flow gain coefficient of valve is identified as 2.825×10-4 m3/(s·A) and the model is verified.展开更多
The realization of the parameter estimation of chirp signals using the fractional Fourier transform (FRFT) is based on the assumption that the sampling duration of practical observed signals would be equal to the time...The realization of the parameter estimation of chirp signals using the fractional Fourier transform (FRFT) is based on the assumption that the sampling duration of practical observed signals would be equal to the time duration of chirp signals contained in the former. However, in many actual circumstances, this assumption seems unreasonable. On the basis of analyzing the practical signal form, this paper derives the estimation error of the existing parameter estimation method and then proposes a novel and universal parameter estimation algorithm. Furthermore, the proposed algorithm is developed which allows the estimation of the practical observed Gaussian windowed chirp signal. Simulation results show that the new algorithm works well.展开更多
Surface roughness parameter is an important factor and obstacle for retrieving soil moisture in passive microwave remote sensing.Two statistical parameters,root mean square (RMS) height (s) and correlation length (l),...Surface roughness parameter is an important factor and obstacle for retrieving soil moisture in passive microwave remote sensing.Two statistical parameters,root mean square (RMS) height (s) and correlation length (l),are designed for describing the roughness of a randomly rough surface.The roughness parameter measured by traditional way is independence of frequency,soil moisture and soil heterogeneity and just the ″geometric″ roughness of random surface.This ″geometric″ roughness can not fully explain the scattered thermal radiation by the earth's surface.The relationship between ″geometric″ roughness and integrated roughness (contain both ″geometric″ roughness and ″dielectric″ roughness) is linked by empirical coefficient.In view of this problem,this paper presents a method for estimating integrated surface roughness from radiometer sampling data at different frequencies,which mainly based on the flourier relationship between power spectral density distribution and spatial autocorrelation function.We can obtain integrated surface roughness at different frequencies by this method.Besides "geometric" roughness,this integrated surface roughness not only contains "dielectric" roughness but also includes frequency dependence.Combined with Q/H model the polarization coupling coefficient can also be obtained for both H and V polarization.Meanwhile,the simulated numerical results show that radiometer with a sensitivity of 0.1 K can distinguish the different surface roughness and the change of roughness with frequency for the same rough surface.This confirms the feasibility of radiometer sampling method for estimating the surface roughness theoretically.This method overcomes the problem of ″dielectric″ roughness measurement to some extent and can achieve the integrated surface roughness within a microwave pixel which can serve soil moisture inversion better than the ″geometric″ roughness.展开更多
It is common for aircraft to encounter atmospheric turbulence in flight tests.Turbulence is usually modeled as stochastic process noise in the flight dynamics equations.In this paper,parameter estimation of nonlinear ...It is common for aircraft to encounter atmospheric turbulence in flight tests.Turbulence is usually modeled as stochastic process noise in the flight dynamics equations.In this paper,parameter estimation of nonlinear dynamic system with both process and measurement noise was studied,and a practical filter error method was proposed.The linearized Kalman filter of first-order approximation was used for state estimation,in which the filter gain,along with the system parameters and the initial states,constituted the parameter vector to be estimated.The unknown parameters and measurement noise covariance were estimated alternately by a relaxation iteration method,and the sensitivities of observations to unknown parameters were calculated by finite difference approximation.Some practical aspects of the method application were discussed.The proposed filter error method was validated by the flight simulation data of a research aircraft.Then,the method was applied to the flight tests of a subscale aircraft,and the aerodynamic stability and control derivatives were estimated.All the estimation results were compared with the results of the output error method to demonstrate the effectiveness of the approach.It is shown that the filter error method is superior to the output error method for flight tests in atmospheric turbulence.展开更多
Effectiveness evaluation of the joint operation system is an important basis for the demonstration and development of weapon equipment.With the consideration that existing models of system effectiveness evaluation sel...Effectiveness evaluation of the joint operation system is an important basis for the demonstration and development of weapon equipment.With the consideration that existing models of system effectiveness evaluation seldom describe the structural relationship among equipment clearly as well as reflect the dynamic,the analog-to-digital converter-graphical evaluation and review technique(ADC-GERT)network parameter estimation model is proposed based on the ADC model and the joint operation system structure.Firstly,analysis of the joint operation system structure and operation process is conducted to build the GERT network,where equipment subsystems are nodes and activities are directed arches.Then the mission effectiveness of equipment subsystems is calculated by the ADC model.The probability transfer parameters are modified by the mission effectiveness of equipment subsystems based on the Bayesian theorem,with the ADC-GERT network parameter estimation model constructed.Finally,a case study is used to validate the efficiency and dynamic of the ADC-GERT network parameter estimation model.展开更多
The multirate multi-input systems have different updating periods and sampling periods such that the conventional identification algorithms cannot be used to identify such multirate systems. By using the auxiliary mod...The multirate multi-input systems have different updating periods and sampling periods such that the conventional identification algorithms cannot be used to identify such multirate systems. By using the auxiliary model identification idea, the multiinnovation stochastic gradient algorithm is developed to estimate the parameters of multirate systems. Finally, an illustrative example is given to verify the effectiveness of the proposed algorithm.展开更多
A new adaptive estimator for direct sequence spread spectrum （DSSS） signals using fourth-order cumulant based adaptive method is considered. The general higher-order statistics may not be easily applied in signal pr...A new adaptive estimator for direct sequence spread spectrum （DSSS） signals using fourth-order cumulant based adaptive method is considered. The general higher-order statistics may not be easily applied in signal processing with too complex computation. Based on the fourth-order cumulant with 1-D slices and adaptive filters, an efficient algorithm is proposed to solve the problem and is extended for nonstationary stochastic processes. In order to achieve the accurate parameter estimation of direct sequence spread spectrum （DSSS） signals, the fast step uses the modified fourth-order cumulant to reduce the computing complexity. While the second step employs an adaptive recursive system to estimate the power spectrum in the frequency domain. In the case of intercepted signals without large enough data samples, the estimator provides good performance in parameter estimation and white Gaussian noise suppression. Computer simulations are included to corroborate the theoretical development with different signal-to-noise ratio conditions and recursive coefficients.展开更多
基金supported by the National Natural Science Foundation of China(grant/award number 52072266).
文摘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.
基金supported in part by the National Natural Science Foundation of China(No.62222120)the National Key Research and Development Program of China(No.2024YFB3909804)the Shandong Provincial Natural Science Foundation(No.ZR2024JQ003).
文摘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.
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The National Natural Science Foundation of China under contract No. 42274159the Project supported by Key Laboratory of Space Ocean Remote Sensing and Application,MNR under contract No.2023CFO016。
文摘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.
基金supported via funding from Prince Sattam Bin Abdulaziz University project number(PSAU/2025/R/1446).
文摘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.
基金supported in part by the National Natural Science Foundation of China(No.62271248)the Natural Science Foundation of Jiangsu Province(No.BK20230090)the Key Laboratory of Land Satellite Remote Sensing Application through the Ministry of Natural Resources of China(No.KLSMNR-K202303).
文摘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.
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.
基金Supported by the Natural Science Foundation of Anhui Education Committee
文摘In this paper, based on the theory of parameter estimation, we give a selection method and, in a sense of a good character of the parameter estimation, we think that it is very reasonable. Moreover, we offer a calculation method of selection statistic and an applied example.
基金National Natural Science Foundation of China(60832012)
文摘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.
基金Project supported by the National Natural Science Foundation of China (Grant Nos. 60473042,60573067 and 60803102)
文摘We study the parameter estimation of a nonlinear chaotic system,which can be essentially formulated as a multidimensional optimization problem.In this paper,an orthogonal learning cuckoo search algorithm is used to estimate the parameters of chaotic systems.This algorithm can combine the stochastic exploration of the cuckoo search and the exploitation capability of the orthogonal learning strategy.Experiments are conducted on the Lorenz system and the Chen system.The proposed algorithm is used to estimate the parameters for these two systems.Simulation results and comparisons demonstrate that the proposed algorithm is better or at least comparable to the particle swarm optimization and the genetic algorithm when considering the quality of the solutions obtained.
基金supported by the National Natural Science Foundation of China(61304254)the National Science Foundation for Distinguished Young Scholars of China(60925011)the Provincial and Ministerial Key Fund of China(9140A07010511BQ0105)
文摘An adaptive unscented Kalman filter(AUKF)and an augmented state method are employed to estimate the timevarying parameters and states of a kind of nonlinear high-speed objects.A strong tracking filter is employed to improve the tracking ability and robustness of unscented Kalman filter(UKF)when the process noise is inaccuracy,and wavelet transform is used to improve the estimate accuracy by the variance of measurement noise.An augmented square-root framework is utilized to improve the numerical stability and accuracy of UKF.Monte Carlo simulations and applications in the rapid trajectory estimation of hypersonic artillery shells confirm the effectiveness of the proposed method.
基金supported by the National Natural Science Foundation of China (61302188)the Nanjing University of Science and Technology Research Foundation (2010ZDJH05)
文摘Time-frequency-based methods are proven to be effective for parameter estimation of linear frequency modulation （LFM） signals. The smoothed pseudo Winger-Ville distribution （SPWVD） is used for the parameter estimation of multi-LFM signals, and a method of the SPWVD binarization by a dynamic threshold based on the Otsu algorithm is proposed. The proposed method is effective in the demand for the estimation of different parameters and the unknown signal-to-noise ratio （SNR） circumstance. The performance of this method is confirmed by numerical simulation.
基金supported by the National Natural Science Foundation of China(6120113461201135)+2 种基金the 111 Project(B08038)the Fundamental Research Funds for the Central Universities(72124669)the Open Research Fund of the Academy of Application(2014CXJJ-TX06)
文摘By using the sparsity of frequency hopping（FH） signals,an underdetermined blind source separation（UBSS） algorithm is presented. Firstly, the short time Fourier transform（STFT） is performed on the mixed signals. Then, the mixing matrix, hopping frequencies, hopping instants and the hooping rate can be estimated by the K-means clustering algorithm. With the estimated mixing matrix, the directions of arrival（DOA） of source signals can be obtained. Then, the FH signals are sorted and the FH pattern is obtained. Finally, the shortest path algorithm is adopted to recover the time domain signals. Simulation results show that the correlation coefficient between the estimated FH signal and the source signal is above 0.9 when the signal-to-noise ratio（SNR） is higher than 0 d B and hopping parameters of multiple FH signals in the synchronous orthogonal FH network can be accurately estimated and sorted under the underdetermined conditions.
基金Project(2003AA430200) supported by the National High-Tech Research and Development Program of China
文摘A retrofitted electro-hydraulic proportional system for hydraulic excavator was introduced firstly. According to the principle and characteristic of load independent flow distribution(LUDV) system,taking boom hydraulic system as an example and ignoring the leakage of hydraulic cylinder and the mass of oil in it,a force equilibrium equation and a continuous equation of hydraulic cylinder were set up. Based on the flow equation of electro-hydraulic proportional valve,the pressure passing through the valve and the difference of pressure were tested and analyzed. The results show that the difference of pressure does not change with load,and it approximates to 2.0 MPa. And then,assume the flow across the valve is directly proportional to spool displacement and is not influenced by load,a simplified model of electro-hydraulic system was put forward. At the same time,by analyzing the structure and load-bearing of boom instrument,and combining moment equivalent equation of manipulator with rotating law,the estimation methods and equations for such parameters as equivalent mass and bearing force of hydraulic cylinder were set up. Finally,the step response of flow of boom cylinder was tested when the electro-hydraulic proportional valve was controlled by the step current. Based on the experiment curve,the flow gain coefficient of valve is identified as 2.825×10-4 m3/(s·A) and the model is verified.
基金supported by the National Natural Science Foundation of China (60872003 61071214)+1 种基金the Doctoral Fund of Ministry of Education of China (20093201110005)the Foundation of Chinese National Defense Technology Key Laboratory (9140C1301031001)
文摘The realization of the parameter estimation of chirp signals using the fractional Fourier transform (FRFT) is based on the assumption that the sampling duration of practical observed signals would be equal to the time duration of chirp signals contained in the former. However, in many actual circumstances, this assumption seems unreasonable. On the basis of analyzing the practical signal form, this paper derives the estimation error of the existing parameter estimation method and then proposes a novel and universal parameter estimation algorithm. Furthermore, the proposed algorithm is developed which allows the estimation of the practical observed Gaussian windowed chirp signal. Simulation results show that the new algorithm works well.
基金Under the auspices of the Key Direction in Knowledge Innovation Programs of Chinese Academy of Sciences (No. KZCX2-YW-340)
文摘Surface roughness parameter is an important factor and obstacle for retrieving soil moisture in passive microwave remote sensing.Two statistical parameters,root mean square (RMS) height (s) and correlation length (l),are designed for describing the roughness of a randomly rough surface.The roughness parameter measured by traditional way is independence of frequency,soil moisture and soil heterogeneity and just the ″geometric″ roughness of random surface.This ″geometric″ roughness can not fully explain the scattered thermal radiation by the earth's surface.The relationship between ″geometric″ roughness and integrated roughness (contain both ″geometric″ roughness and ″dielectric″ roughness) is linked by empirical coefficient.In view of this problem,this paper presents a method for estimating integrated surface roughness from radiometer sampling data at different frequencies,which mainly based on the flourier relationship between power spectral density distribution and spatial autocorrelation function.We can obtain integrated surface roughness at different frequencies by this method.Besides "geometric" roughness,this integrated surface roughness not only contains "dielectric" roughness but also includes frequency dependence.Combined with Q/H model the polarization coupling coefficient can also be obtained for both H and V polarization.Meanwhile,the simulated numerical results show that radiometer with a sensitivity of 0.1 K can distinguish the different surface roughness and the change of roughness with frequency for the same rough surface.This confirms the feasibility of radiometer sampling method for estimating the surface roughness theoretically.This method overcomes the problem of ″dielectric″ roughness measurement to some extent and can achieve the integrated surface roughness within a microwave pixel which can serve soil moisture inversion better than the ″geometric″ roughness.
基金supported by the National Natural Science Foundation of China(No.11802325)。
文摘It is common for aircraft to encounter atmospheric turbulence in flight tests.Turbulence is usually modeled as stochastic process noise in the flight dynamics equations.In this paper,parameter estimation of nonlinear dynamic system with both process and measurement noise was studied,and a practical filter error method was proposed.The linearized Kalman filter of first-order approximation was used for state estimation,in which the filter gain,along with the system parameters and the initial states,constituted the parameter vector to be estimated.The unknown parameters and measurement noise covariance were estimated alternately by a relaxation iteration method,and the sensitivities of observations to unknown parameters were calculated by finite difference approximation.Some practical aspects of the method application were discussed.The proposed filter error method was validated by the flight simulation data of a research aircraft.Then,the method was applied to the flight tests of a subscale aircraft,and the aerodynamic stability and control derivatives were estimated.All the estimation results were compared with the results of the output error method to demonstrate the effectiveness of the approach.It is shown that the filter error method is superior to the output error method for flight tests in atmospheric turbulence.
基金supported by the National Natural Science Foundation of China(72071111,71801127,71671091)the NSFC and the UK Royal Society joint project(71811530338)+2 种基金the Special Postdoctoral Fund of China(2019TQ0150)the Fundamental Research Funds for the Central Universities of China(NC2019003)the Intelligence Introduction Base of the Ministry of Science and Technology(G20190010178)。
文摘Effectiveness evaluation of the joint operation system is an important basis for the demonstration and development of weapon equipment.With the consideration that existing models of system effectiveness evaluation seldom describe the structural relationship among equipment clearly as well as reflect the dynamic,the analog-to-digital converter-graphical evaluation and review technique(ADC-GERT)network parameter estimation model is proposed based on the ADC model and the joint operation system structure.Firstly,analysis of the joint operation system structure and operation process is conducted to build the GERT network,where equipment subsystems are nodes and activities are directed arches.Then the mission effectiveness of equipment subsystems is calculated by the ADC model.The probability transfer parameters are modified by the mission effectiveness of equipment subsystems based on the Bayesian theorem,with the ADC-GERT network parameter estimation model constructed.Finally,a case study is used to validate the efficiency and dynamic of the ADC-GERT network parameter estimation model.
基金supported by the National Natural Science Foundation of China (60973043)
文摘The multirate multi-input systems have different updating periods and sampling periods such that the conventional identification algorithms cannot be used to identify such multirate systems. By using the auxiliary model identification idea, the multiinnovation stochastic gradient algorithm is developed to estimate the parameters of multirate systems. Finally, an illustrative example is given to verify the effectiveness of the proposed algorithm.
文摘A new adaptive estimator for direct sequence spread spectrum （DSSS） signals using fourth-order cumulant based adaptive method is considered. The general higher-order statistics may not be easily applied in signal processing with too complex computation. Based on the fourth-order cumulant with 1-D slices and adaptive filters, an efficient algorithm is proposed to solve the problem and is extended for nonstationary stochastic processes. In order to achieve the accurate parameter estimation of direct sequence spread spectrum （DSSS） signals, the fast step uses the modified fourth-order cumulant to reduce the computing complexity. While the second step employs an adaptive recursive system to estimate the power spectrum in the frequency domain. In the case of intercepted signals without large enough data samples, the estimator provides good performance in parameter estimation and white Gaussian noise suppression. Computer simulations are included to corroborate the theoretical development with different signal-to-noise ratio conditions and recursive coefficients.
