In target tracking applications,the Doppler measurement contains information of the target range rate,which has the potential capability to improve the tracking performance.However,the nonlinear degree between the mea...In target tracking applications,the Doppler measurement contains information of the target range rate,which has the potential capability to improve the tracking performance.However,the nonlinear degree between the measurement and the target state increases with the introduction of the Doppler measurement.Therefore,target tracking in the Doppler radar is a nonlinear filtering problem.In order to handle this problem,the Kalman filter form of best linear unbiased estimation(BLUE)with position measurements is proposed,which is combined with the sequential filtering algorithm to handle the Doppler measurement further,where the statistic characteristic of the converted measurement error is calculated based on the predicted information in the sequential filter.Moreover,the algorithm is extended to the maneuvering target tracking case,where the interacting multiple model(IMM)algorithm is used as the basic framework and the model probabilities are updated according to the BLUE position filter and the sequential filter,and the final estimation is a weighted sum of the outputs from the sequential filters and the model probabilities.Simulation results show that compared with existing approaches,the proposed algorithm can realize target tracking with preferable tracking precision and the extended method can achieve effective maneuvering target tracking.展开更多
Sequential measurement processing is of benefit to both estimation accuracy and computational efficiency. When the noises are correlated across the measurement components, decorrelation based on covariance matrix fact...Sequential measurement processing is of benefit to both estimation accuracy and computational efficiency. When the noises are correlated across the measurement components, decorrelation based on covariance matrix factorization is required in the previous methods in order to perform sequential updates properly. A new sequential processing method, which carries out the sequential updates directly using the correlated measurement components, is proposed. And a typical sequential processing example is investigated, where the converted position measure- ments are used to estimate target states by standard Kalman filtering equations and the converted Doppler measurements are then incorporated into a minimum mean squared error （MMSE） estimator with the updated cross-covariance involved to account for the correlated errors. Numerical simulations demonstrate the superiority of the proposed new sequential processing in terms of better accuracy and consistency than the conventional sequential filter based on measurement decorrelation.展开更多
To obtain higher accurate position estimates, the stochastic model is estimated by using residual of observations, hence, the stochastic model describes the noise and bias in measurements more realistically. By using ...To obtain higher accurate position estimates, the stochastic model is estimated by using residual of observations, hence, the stochastic model describes the noise and bias in measurements more realistically. By using GPS data and broadcast ephemeris, the numerical results indicating the accurate position estimates at sub-meter level are obtainable.展开更多
This paper presents a novel optimization technique for an efficient multi-fidelity model building approach to reduce computational costs for handling aerodynamic shape optimization based on high-fidelity simulation mo...This paper presents a novel optimization technique for an efficient multi-fidelity model building approach to reduce computational costs for handling aerodynamic shape optimization based on high-fidelity simulation models. The wing aerodynamic shape optimization problem is solved by dividing optimization into three steps—modeling 3D（high-fidelity） and 2D（lowfidelity） models, building global meta-models from prominent instead of all variables, and determining robust optimizing shape associated with tuning local meta-models. The adaptive robust design optimization aims to modify the shape optimization process. The sufficient infilling strategy—known as adaptive uniform infilling strategy—determines search space dimensions based on the last optimization results or initial point. Following this, 3D model simulations are used to tune local meta-models. Finally, the global optimization gradient-based method—Adaptive Filter Sequential Quadratic Programing（AFSQP） is utilized to search the neighborhood for a probable optimum point. The effectiveness of the proposed method is investigated by applying it, along with conventional optimization approach-based meta-models, to a Blended Wing Body（BWB） Unmanned Aerial Vehicle（UAV）. The drag coefficient is defined as the objective function, which is subjected to minimum lift coefficient bounds and stability constraints. The simulation results indicate improvement in meta-model accuracy and reduction in computational time of the method introduced in this paper.展开更多
Detection and tracking of multi-target with unknown and varying number is a challenging issue, especially under the condition of low signal-to-noise ratio(SNR). A modified multi-target track-before-detect(TBD) method ...Detection and tracking of multi-target with unknown and varying number is a challenging issue, especially under the condition of low signal-to-noise ratio(SNR). A modified multi-target track-before-detect(TBD) method was proposed to tackle this issue using a nonstandard point observation model. The method was developed from sequential Monte Carlo(SMC)-based probability hypothesis density(PHD) filter, and it was implemented by modifying the original calculation in update weights of the particles and by adopting an adaptive particle sampling strategy. To efficiently execute the SMC-PHD based TBD method, a fast implementation approach was also presented by partitioning the particles into multiple subsets according to their position coordinates in 2D resolution cells of the sensor. Simulation results show the effectiveness of the proposed method for time-varying multi-target tracking using raw observation data.展开更多
Chlorospermines A and B are biologically interesting acridone natural products and recently isolated from Glycosmis chlorosperma.We report here a convergent approach to construct the tetracyclic core of the natural pr...Chlorospermines A and B are biologically interesting acridone natural products and recently isolated from Glycosmis chlorosperma.We report here a convergent approach to construct the tetracyclic core of the natural products.The two fragments are assembled together through Sonogashira coupling,and a cis-triene intermediate was prepared by using hydrosilylation/desilylation.A 6p-electrocyclization/aromatization sequence served as the key step of the synthesis,which formed the tetrasubstituted arene motif in one pot.展开更多
Magnetometer is a highly advantageous sensor for determining a spacecraft’s attitude.This article provides a solution to the problem of spacecraft attitude estimation using magnetometer measurements only.To ensure fu...Magnetometer is a highly advantageous sensor for determining a spacecraft’s attitude.This article provides a solution to the problem of spacecraft attitude estimation using magnetometer measurements only.To ensure full observability of spacecraft attitude states,it is necessary to use at least two types of sensors.Consequently,utilizing a single sensor,such as the magnetometer,poses a significant challenge for any attitude estimation algorithm,including the extended Kalman filter(EKF).Moreover,implementing the EKF algorithm,or any other attitude estimation algorithm,is computationally intensive.To address these issues,an algorithm has been developed that estimates spacecraft attitude angles and attitude rates using a sequential extended Kalman filter(SEKF).This algorithm offers numerous benefits over those found in the literature such as high accuracy,low computational resource requirements,the ability to converge even with large initial attitude and angular velocity estimation errors,and the ability to function even if two of the three measurement channels of the magnetometer are not functioning.With these benefits,the developed SEKF algorithm is capable of operating in all spacecraft operational modes,delivering accurate performance and computation time.In spite of measurements with large noise values,the high accuracy achieved by the SEKF algorithm enables the magnetometer to serve as the sole source of attitude information,even if one or two magnetometer measurement channels are not functioning.展开更多
基金This work was supported by the Basic Research Operation Foundation for Central University(ZYGX2016J039).
文摘In target tracking applications,the Doppler measurement contains information of the target range rate,which has the potential capability to improve the tracking performance.However,the nonlinear degree between the measurement and the target state increases with the introduction of the Doppler measurement.Therefore,target tracking in the Doppler radar is a nonlinear filtering problem.In order to handle this problem,the Kalman filter form of best linear unbiased estimation(BLUE)with position measurements is proposed,which is combined with the sequential filtering algorithm to handle the Doppler measurement further,where the statistic characteristic of the converted measurement error is calculated based on the predicted information in the sequential filter.Moreover,the algorithm is extended to the maneuvering target tracking case,where the interacting multiple model(IMM)algorithm is used as the basic framework and the model probabilities are updated according to the BLUE position filter and the sequential filter,and the final estimation is a weighted sum of the outputs from the sequential filters and the model probabilities.Simulation results show that compared with existing approaches,the proposed algorithm can realize target tracking with preferable tracking precision and the extended method can achieve effective maneuvering target tracking.
基金supported by the National Natural Science Foundation of China(6120131161132005)the Aerospace Science Foundation of China(20142077010)
文摘Sequential measurement processing is of benefit to both estimation accuracy and computational efficiency. When the noises are correlated across the measurement components, decorrelation based on covariance matrix factorization is required in the previous methods in order to perform sequential updates properly. A new sequential processing method, which carries out the sequential updates directly using the correlated measurement components, is proposed. And a typical sequential processing example is investigated, where the converted position measure- ments are used to estimate target states by standard Kalman filtering equations and the converted Doppler measurements are then incorporated into a minimum mean squared error （MMSE） estimator with the updated cross-covariance involved to account for the correlated errors. Numerical simulations demonstrate the superiority of the proposed new sequential processing in terms of better accuracy and consistency than the conventional sequential filter based on measurement decorrelation.
基金Supported by the National 863 Program of China （No.2006AA12Z325） and the National Natural Science Foundation of China （No.40274005）.
文摘To obtain higher accurate position estimates, the stochastic model is estimated by using residual of observations, hence, the stochastic model describes the noise and bias in measurements more realistically. By using GPS data and broadcast ephemeris, the numerical results indicating the accurate position estimates at sub-meter level are obtainable.
文摘This paper presents a novel optimization technique for an efficient multi-fidelity model building approach to reduce computational costs for handling aerodynamic shape optimization based on high-fidelity simulation models. The wing aerodynamic shape optimization problem is solved by dividing optimization into three steps—modeling 3D（high-fidelity） and 2D（lowfidelity） models, building global meta-models from prominent instead of all variables, and determining robust optimizing shape associated with tuning local meta-models. The adaptive robust design optimization aims to modify the shape optimization process. The sufficient infilling strategy—known as adaptive uniform infilling strategy—determines search space dimensions based on the last optimization results or initial point. Following this, 3D model simulations are used to tune local meta-models. Finally, the global optimization gradient-based method—Adaptive Filter Sequential Quadratic Programing（AFSQP） is utilized to search the neighborhood for a probable optimum point. The effectiveness of the proposed method is investigated by applying it, along with conventional optimization approach-based meta-models, to a Blended Wing Body（BWB） Unmanned Aerial Vehicle（UAV）. The drag coefficient is defined as the objective function, which is subjected to minimum lift coefficient bounds and stability constraints. The simulation results indicate improvement in meta-model accuracy and reduction in computational time of the method introduced in this paper.
基金Projects(61002022,61471370)supported by the National Natural Science Foundation of China
文摘Detection and tracking of multi-target with unknown and varying number is a challenging issue, especially under the condition of low signal-to-noise ratio(SNR). A modified multi-target track-before-detect(TBD) method was proposed to tackle this issue using a nonstandard point observation model. The method was developed from sequential Monte Carlo(SMC)-based probability hypothesis density(PHD) filter, and it was implemented by modifying the original calculation in update weights of the particles and by adopting an adaptive particle sampling strategy. To efficiently execute the SMC-PHD based TBD method, a fast implementation approach was also presented by partitioning the particles into multiple subsets according to their position coordinates in 2D resolution cells of the sensor. Simulation results show the effectiveness of the proposed method for time-varying multi-target tracking using raw observation data.
基金Ministry of Science & Technology (No.2013CB836900)National Natural Science Foundation of China (Nos.21290180,21172235 and 21222202)China Postdoctoral Science Foundation (No.2014M561537,M.Y.)
文摘Chlorospermines A and B are biologically interesting acridone natural products and recently isolated from Glycosmis chlorosperma.We report here a convergent approach to construct the tetracyclic core of the natural products.The two fragments are assembled together through Sonogashira coupling,and a cis-triene intermediate was prepared by using hydrosilylation/desilylation.A 6p-electrocyclization/aromatization sequence served as the key step of the synthesis,which formed the tetrasubstituted arene motif in one pot.
基金supported by the National Authority for Remote Sensing and Space Sciences(NARSS)under project code 0725/SR/SPA/2022MATLAB license is provided by Nile University.Language editing service is provided via topmost AI software,ChatGPT.
文摘Magnetometer is a highly advantageous sensor for determining a spacecraft’s attitude.This article provides a solution to the problem of spacecraft attitude estimation using magnetometer measurements only.To ensure full observability of spacecraft attitude states,it is necessary to use at least two types of sensors.Consequently,utilizing a single sensor,such as the magnetometer,poses a significant challenge for any attitude estimation algorithm,including the extended Kalman filter(EKF).Moreover,implementing the EKF algorithm,or any other attitude estimation algorithm,is computationally intensive.To address these issues,an algorithm has been developed that estimates spacecraft attitude angles and attitude rates using a sequential extended Kalman filter(SEKF).This algorithm offers numerous benefits over those found in the literature such as high accuracy,low computational resource requirements,the ability to converge even with large initial attitude and angular velocity estimation errors,and the ability to function even if two of the three measurement channels of the magnetometer are not functioning.With these benefits,the developed SEKF algorithm is capable of operating in all spacecraft operational modes,delivering accurate performance and computation time.In spite of measurements with large noise values,the high accuracy achieved by the SEKF algorithm enables the magnetometer to serve as the sole source of attitude information,even if one or two magnetometer measurement channels are not functioning.
