In this paper, we extend matrix scaled total least squares (MSTLS) problem with a single right-hand side to the case of multiple right-hand sides. Firstly, under some mild conditions, this paper gives an explicit expr...In this paper, we extend matrix scaled total least squares (MSTLS) problem with a single right-hand side to the case of multiple right-hand sides. Firstly, under some mild conditions, this paper gives an explicit expression of the minimum norm solution of MSTLS problem with multiple right-hand sides. Then, we present the Kronecker-product-based formulae for the normwise, mixed and componentwise condition numbers of the MSTLS problem. For easy estimation, we also exhibit Kronecker-product-free upper bounds for these condition numbers. All these results can reduce to those of the total least squares (TLS) problem which were given by Zheng <em>et al</em>. Finally, two numerical experiments are performed to illustrate our results.展开更多
When the total least squares(TLS)solution is used to solve the parameters in the errors-in-variables(EIV)model,the obtained parameter estimations will be unreliable in the observations containing systematic errors.To ...When the total least squares(TLS)solution is used to solve the parameters in the errors-in-variables(EIV)model,the obtained parameter estimations will be unreliable in the observations containing systematic errors.To solve this problem,we propose to add the nonparametric part(systematic errors)to the partial EIV model,and build the partial EIV model to weaken the influence of systematic errors.Then,having rewritten the model as a nonlinear model,we derive the formula of parameter estimations based on the penalized total least squares criterion.Furthermore,based on the second-order approximation method of precision estimation,we derive the second-order bias and covariance of parameter estimations and calculate the mean square error(MSE).Aiming at the selection of the smoothing factor,we propose to use the U curve method.The experiments show that the proposed method can mitigate the influence of systematic errors to a certain extent compared with the traditional method and get more reliable parameter estimations and its precision information,which validates the feasibility and effectiveness of the proposed method.展开更多
Through theoretical derivation, some properties of the total least squares estimation are found. The total least squares estimation is the linear transformation of the least squares estimation, and the total least squ...Through theoretical derivation, some properties of the total least squares estimation are found. The total least squares estimation is the linear transformation of the least squares estimation, and the total least squares estimation is unbiased. The condition number of the total least squares estimation is greater than the least squares estimation, so the total least squares estimation is easier to be affected by the data error than the least squares estimation. Then through the further derivation, the relationships of solutions, residuals and unit weight variance estimations between the total least squares and the least squares are given.展开更多
Based on the constrained total least squares （CTLS） passive location algorithm with bearing-only measurements, in this paper, the same passive location problem is transformed into the structured total least squares ...Based on the constrained total least squares （CTLS） passive location algorithm with bearing-only measurements, in this paper, the same passive location problem is transformed into the structured total least squares （STLS） problem.The solution of the STLS problem for passive location can be obtained using the inverse iteration method.It also expatiates that both the STLS algorithm and the CTLS algorithm have the same location mean squares error under certain condition.Finally, the article presents a kind of location and tracking algorithm for moving target by combining STLS location algorithm with Kalman filter （KF）.The efficiency and superiority of the proposed algorithms can be confirmed by computer simulation results.展开更多
Single-station passive localization technology avoids the complex time synchronization and information exchange between multiple observatories, and is increasingly important in electronic warfare. Based on a single mo...Single-station passive localization technology avoids the complex time synchronization and information exchange between multiple observatories, and is increasingly important in electronic warfare. Based on a single moving station localization system, a new method with high localization precision and numerical stability is proposed when the measurements from multiple disjoint sources are subject to the same station position and velocity displacement. According to the available measurements including the angle-of-arrival(AOA), time-of-arrival(TOA), and frequency-of-arrival(FOA), the corresponding pseudo linear equations are deduced. Based on this, a structural total least squares(STLS) optimization model is developed and the inverse iteration algorithm is used to obtain the stationary target location. The localization performance of the STLS localization algorithm is derived, and it is strictly proved that the theoretical performance of the STLS method is consistent with that of the constrained total least squares method under first-order error analysis, both of which can achieve the Cramér-Rao lower bound accuracy. Simulation results show the validity of the theoretical derivation and superiority of the new algorithm.展开更多
The constrained total least squares algorithm for the passive location is presented based on the bearing-only measurements in this paper. By this algorithm the non-linear measurement equations are firstly transformed ...The constrained total least squares algorithm for the passive location is presented based on the bearing-only measurements in this paper. By this algorithm the non-linear measurement equations are firstly transformed into linear equations and the effect of the measurement noise on the linear equation coefficients is analyzed, therefore the problem of the passive location can be considered as the problem of constrained total least squares, then the problem is changed into the optimized question without restraint which can be solved by the Newton algorithm, and finally the analysis of the location accuracy is given. The simulation results prove that the new algorithm is effective and practicable.展开更多
A new method for Total Least Squares (TLS) problems is presented. It differs from previous approaches and is based on the solution of successive Least Squares problems. The method is quite suitable for Structured TLS ...A new method for Total Least Squares (TLS) problems is presented. It differs from previous approaches and is based on the solution of successive Least Squares problems. The method is quite suitable for Structured TLS (STLS) problems. We study mostly the case of Toeplitz matrices in this paper. The numerical tests illustrate that the method converges to the solution fast for Toeplitz STLS problems. Since the method is designed for general TLS problems, other structured problems can be treated similarly.展开更多
Least squares support vector machines (LS-SVMs) are modified support vector machines (SVMs) that involve equality constraints and work with a least squares cost function, which simplifies the optimization procedure. I...Least squares support vector machines (LS-SVMs) are modified support vector machines (SVMs) that involve equality constraints and work with a least squares cost function, which simplifies the optimization procedure. In this paper, a novel training algorithm based on total least squares (TLS) for an LS-SVM is presented and applied to multifunctional sensor signal reconstruction. For three different nonlinearities of a multifunctional sensor model, the reconstruction accuracies of input signals are 0.001 36%, 0.031 84% and 0.504 80%, respectively. The experimental results demonstrate the higher reliability and accuracy of the proposed method for multifunctional sensor signal reconstruction than the original LS-SVM training algorithm, and verify the feasibility and stability of the proposed method.展开更多
A new approach called the robust structured total least squares(RSTLS) algorithm is described for solving location inaccuracy caused by outliers in the single-observer passive location. It is built within the weighted...A new approach called the robust structured total least squares(RSTLS) algorithm is described for solving location inaccuracy caused by outliers in the single-observer passive location. It is built within the weighted structured total least squares(WSTLS)framework and improved based on the robust estimation theory.Moreover, the improved Danish weight function is proposed according to the robust extremal function of the WSTLS, so that the new algorithm can detect outliers based on residuals and reduce the weights of outliers automatically. Finally, the inverse iteration method is discussed to deal with the RSTLS problem. Simulations show that when outliers appear, the result of the proposed algorithm is still accurate and robust, whereas that of the conventional algorithms is distorted seriously.展开更多
In order to solve the bearings-only passive localization problem in the presence of erroneous observer position, a novel algorithm based on double side matrix-restricted total least squares （DSMRTLS） is proposed. Fi...In order to solve the bearings-only passive localization problem in the presence of erroneous observer position, a novel algorithm based on double side matrix-restricted total least squares （DSMRTLS） is proposed. First, the aforementioned passive localization problem is transferred to the DSMRTLS problem by deriving a multiplicative structure for both the observation matrix and the observation vector. Second, the corresponding optimization problem of the DSMRTLS problem without constraint is derived, which can be approximated as the generalized Rayleigh quotient minimization problem. Then, the localization solution which is globally optimal and asymptotically unbiased can be got by generalized eigenvalue decomposition. Simulation results verify the rationality of the approximation and the good performance of the proposed algorithm compared with several typical algorithms.展开更多
The interrelationship between the ARMA model parameters of a vented-box loud-speaker system and lowthequency characteristic parameters is analysed in this paper. These AANA model parameters are deterndned by the total...The interrelationship between the ARMA model parameters of a vented-box loud-speaker system and lowthequency characteristic parameters is analysed in this paper. These AANA model parameters are deterndned by the total least squares (TLS) method. Therefore, we can measure in the timedomain the fow frequency characteristic parameters of a vented-box loudspeaker system, its impendance curvs and low-frquency response curves. These measured results show a satisfactory agreement with the values obtained by the frequency-domain measurement.展开更多
超声医学成像因非侵入式、成本低且实时性好而被广泛应用。超声波动方程具有严重的离散不适定问题,迭代化与正则化结合可以解决这一问题,但重建图像质量较差。为解决这一问题,本文在矩量法基础上,引入Tikhonov-Gaussian方法中的滤波因子...超声医学成像因非侵入式、成本低且实时性好而被广泛应用。超声波动方程具有严重的离散不适定问题,迭代化与正则化结合可以解决这一问题,但重建图像质量较差。为解决这一问题,本文在矩量法基础上,引入Tikhonov-Gaussian方法中的滤波因子,用于校正较小奇异值,将复杂的不适定问题转化为容易求解的最小二乘问题,重构出高质量图像。通过实验数据分析,改进后的截断完全最小二乘算法(truncated total least squares,TTLS)重建质量更高,效果更好,相对误差降低了1.16201%,峰值信噪比提高了0.29132%,信噪比提高了3.0269%,结构相似性提高了1.72531%,图像对比度提高了14.21319%。展开更多
Online parameter identification is essential for the accuracy of the battery equivalent circuit model(ECM).The traditional recursive least squares(RLS)method is easily biased with the noise disturbances from sensors,w...Online parameter identification is essential for the accuracy of the battery equivalent circuit model(ECM).The traditional recursive least squares(RLS)method is easily biased with the noise disturbances from sensors,which degrades the modeling accuracy in practice.Meanwhile,the recursive total least squares(RTLS)method can deal with the noise interferences,but the parameter slowly converges to the reference with initial value uncertainty.To alleviate the above issues,this paper proposes a co-estimation framework utilizing the advantages of RLS and RTLS for a higher parameter identification performance of the battery ECM.RLS converges quickly by updating the parameters along the gradient of the cost function.RTLS is applied to attenuate the noise effect once the parameters have converged.Both simulation and experimental results prove that the proposed method has good accuracy,a fast convergence rate,and also robustness against noise corruption.展开更多
Because the real input acceleration cannot be obtained during the error model identification of inertial navigation platform, both the input and output data contain noises. In this case, the conventional regression mo...Because the real input acceleration cannot be obtained during the error model identification of inertial navigation platform, both the input and output data contain noises. In this case, the conventional regression model and the least squares （LS） method will result in bias. Based on the models of inertial navigation platform error and observation error, the errors-in-variables （EV） model and the total least squares （TLS） method axe proposed to identify the error model of the inertial navigation platform. The estimation precision is improved and the result is better than the conventional regression model based LS method. The simulation results illustrate the effectiveness of the proposed method.展开更多
The Gauss-Markov （GM） model and the Errors-in-Variables （EIV） model are frequently used to perform 3D coordinate transformations in geodesy and engineering surveys. In these applications, because the observation e...The Gauss-Markov （GM） model and the Errors-in-Variables （EIV） model are frequently used to perform 3D coordinate transformations in geodesy and engineering surveys. In these applications, because the observation errors in original coordinates system are also taken into account, the latter is more accurate and reasonable than the former. Although the Weighted Total Least Squares （WTLS） technique has been intro- duced into coordinate transformations as the measured points are heteroscedastic and correlated, the Variance- Covariance Matrix （VCM） of observations is restricted by a particular structure, namely, only the correlations of each points are taken into account. Because the 3D datum transformation with large rotation angle is a non- linear problem, the WTLS is no longer suitable in this ease. In this contribution, we suggested the nonlinear WTLS adjustments with equality constraints （NWTLS-EC） for 3D datum transformation with large rotation an- gle, which removed the particular structure restriction on the VCM. The Least Squares adjustment with Equality （LSE） constraints is employed to solve NWTLS-EC as the nonlinear model has been linearized, and an iterative algorithm is proposed with the LSE solution. A simulation study of 3D datum transformation with large rotation angle is given to insight into the feasibility of our algorithm at last.展开更多
Significant wave height（SWH） can be computed from the returning waveform of radar altimeter, this parameter is only raw estimates if it does not calibrate. But accurate calibration is important for all applications,...Significant wave height（SWH） can be computed from the returning waveform of radar altimeter, this parameter is only raw estimates if it does not calibrate. But accurate calibration is important for all applications, especially for climate studies. HY-2a altimeter has been operational since April 2012 and its products are available to the scientific community. In this work, SWH data from HY-2A altimeters are calibrated against in situ buoy data from the National Data Buoy Center（NDBC）, Distinguished from previous calibration studies which generally regarded buoy data as ＂truth＂, the work of calibration for HY-2A altimeter wave data against in situ buoys was applied a more sophisticated statistical technique-the total least squares（TLS） method which can take into account errors in both variables. We present calibration results for HY-2A radar altimeter measurement of wave height against NDBC buoys. In addition, cross-calibration for HY-2A and Jason-2 wave data are talked over and the result is given.展开更多
This paper presents a new method of improving Global Positioning System(GPS)positioning precision. Based on the altitude hold mode, the method does not need any other equipment. Under this constraint condition, the To...This paper presents a new method of improving Global Positioning System(GPS)positioning precision. Based on the altitude hold mode, the method does not need any other equipment. Under this constraint condition, the Total Least Squares(TLS) algorithm is used to prove that the method is effective. Theoretical analysis shows that the algorithm can significantly improve the GPS positioning precision.展开更多
Coordinates are basic needs for both geospatial and non-geospatial professionals and as a result, geodesists have the responsibility to develop methods that are applicable and practicable for determining cartesian coo...Coordinates are basic needs for both geospatial and non-geospatial professionals and as a result, geodesists have the responsibility to develop methods that are applicable and practicable for determining cartesian coordinates either through transformation, conversion or prediction for the geo-scientific community. It is therefore necessary to implement mechanisms and systems that can be employed to predict coordinates in either two dimensional （2D） or three dimensional （3D） spaces. Artificial Intelligence （AI） techniques and conventional methods within the last decade have been proposed as an effective tool for modeling and forecasting in various scientific disciplines for solving majority of problems. The primary objective of this work is to compare the efficiency of artificial intelligence technique （Feed Forward Back propagation Neural Network （FFBPNN）） and conventional methods （Ordinary Least Squares （OLS）, General Least Squares （GLS）, and Total Least Squares （TLS）） in cartesian planimetric coordinate＇s prediction. In addition, a hybrid approach of conventional and artificial intelligence method thus, TLS-FFBPNN has been proposed in this study for 2D cartesian coordinates prediction. It was observed from the results obtained that FFBPNN performed significantly better than the conventional methods. However, the TLS-FFBPNN when compared with FFBPNN, OLS, GLS and TLS gave stronger and better performance and superior predictions. To further confirm the superiority of the TLS-FFBPNN the Bayesian Information Criterion was introduced. The BIC selected the TLS-FFBPNN as the optimum model for prediction.展开更多
The reliability theory has been an important element of the classical geodetic adjustment theory and methods in the linear Gauss-Markov model. Although errors-in-variables(EIV) models have been intensively investigate...The reliability theory has been an important element of the classical geodetic adjustment theory and methods in the linear Gauss-Markov model. Although errors-in-variables(EIV) models have been intensively investigated, little has been done about reliability theory for EIV models. This paper first investigates the effect of a random coefficient matrix A on the conventional geodetic reliability measures as if the coefficient matrix were deterministic. The effects of such geodetic internal and external reliability measures due to the randomness of the coefficient matrix are worked out, which are shown to depend not only on the noise level of the random elements of A but also on the values of parameters. An alternative, linear approximate reliability theory is accordingly developed for use in EIV models. Both the EIV-affected reliability measures and the corresponding linear approximate measures fully account for the random errors of both the coefficient matrix and the observations, though formulated in a slightly different way. Numerical experiments have been carried to demonstrate the effects of errors-in-variables on reliability measures and compared with the conventional Baarda's reliability measures. The simulations have confirmed our theoretical results that the EIV-reliability measures depend on both the noise level of A and the parameter values. The larger the noise level of A, the larger the EIV-affected internal and external reliability measures;the larger the parameters,the larger the EIV-affected internal and external reliability measures.展开更多
With extensive applications of space geodesy, three-dimensional datum transformation model has been necessarily used to transform the coordinates in the different coordinate systems.Its essence is to predict the coord...With extensive applications of space geodesy, three-dimensional datum transformation model has been necessarily used to transform the coordinates in the different coordinate systems.Its essence is to predict the coordinates of non-common points in the second coordinate system based on their coordinates in the first coordinate system and the coordinates of common points in two coordinate systems.Traditionally, the computation of seven transformation parameters and the transformation of noncommon points are individually implemented, in which the errors of coordinates are taken into account only in the second system although the coordinates in both two systems are inevitably contaminated by the random errors.Moreover, the coordinate errors of non-common points are disregarded when they are transformed using the solved transformation parameters.Here we propose the seamless (rigorous) datum transformation model to compute the transformation parameters and transform the non-common points integratively, considering the errors of all coordinates in both coordinate systems.As a result, a nonlinear coordinate transformation model is formulated.Based on the Gauss-Newton algorithm and the numerical characteristics of transformation parameters, two linear versions of the established nonlinear model are individually derived.Then the least-squares collocation (prediction) method is employed to trivially solve these linear models.Finally, the simulation experiment is carried out to demonstrate the performance and benefits of the presented method.The results show that the presented method can significantly improve the precision of the coordinate transformation, especially when the non-common points are strongly correlated with the common points used to compute the transformation parameters.展开更多
文摘In this paper, we extend matrix scaled total least squares (MSTLS) problem with a single right-hand side to the case of multiple right-hand sides. Firstly, under some mild conditions, this paper gives an explicit expression of the minimum norm solution of MSTLS problem with multiple right-hand sides. Then, we present the Kronecker-product-based formulae for the normwise, mixed and componentwise condition numbers of the MSTLS problem. For easy estimation, we also exhibit Kronecker-product-free upper bounds for these condition numbers. All these results can reduce to those of the total least squares (TLS) problem which were given by Zheng <em>et al</em>. Finally, two numerical experiments are performed to illustrate our results.
基金supported by the National Natural Science Foundation of China,Nos.41874001 and 41664001Support Program for Outstanding Youth Talents in Jiangxi Province,No.20162BCB23050National Key Research and Development Program,No.2016YFB0501405。
文摘When the total least squares(TLS)solution is used to solve the parameters in the errors-in-variables(EIV)model,the obtained parameter estimations will be unreliable in the observations containing systematic errors.To solve this problem,we propose to add the nonparametric part(systematic errors)to the partial EIV model,and build the partial EIV model to weaken the influence of systematic errors.Then,having rewritten the model as a nonlinear model,we derive the formula of parameter estimations based on the penalized total least squares criterion.Furthermore,based on the second-order approximation method of precision estimation,we derive the second-order bias and covariance of parameter estimations and calculate the mean square error(MSE).Aiming at the selection of the smoothing factor,we propose to use the U curve method.The experiments show that the proposed method can mitigate the influence of systematic errors to a certain extent compared with the traditional method and get more reliable parameter estimations and its precision information,which validates the feasibility and effectiveness of the proposed method.
基金The research was supported by the National Natural Science Foundation of China(41204003)Scientific Research Foundation of ECIT(DHBK201113)Scientific Research Foundation of Jiangxi Province Key Laboratory for Digital Land(DLLJ201207)
文摘Through theoretical derivation, some properties of the total least squares estimation are found. The total least squares estimation is the linear transformation of the least squares estimation, and the total least squares estimation is unbiased. The condition number of the total least squares estimation is greater than the least squares estimation, so the total least squares estimation is easier to be affected by the data error than the least squares estimation. Then through the further derivation, the relationships of solutions, residuals and unit weight variance estimations between the total least squares and the least squares are given.
文摘Based on the constrained total least squares （CTLS） passive location algorithm with bearing-only measurements, in this paper, the same passive location problem is transformed into the structured total least squares （STLS） problem.The solution of the STLS problem for passive location can be obtained using the inverse iteration method.It also expatiates that both the STLS algorithm and the CTLS algorithm have the same location mean squares error under certain condition.Finally, the article presents a kind of location and tracking algorithm for moving target by combining STLS location algorithm with Kalman filter （KF）.The efficiency and superiority of the proposed algorithms can be confirmed by computer simulation results.
基金Project supported by the National Natural Science Foundation of China(Nos.61201381,61401513,and 61772548)the China Postdoctoral Science Foundation(No.2016M592989)+1 种基金the Self-Topic Foundation of Information Engineering University,China(No.2016600701)the Outstanding Youth Foundation of Information Engineering University,China(No.2016603201)
文摘Single-station passive localization technology avoids the complex time synchronization and information exchange between multiple observatories, and is increasingly important in electronic warfare. Based on a single moving station localization system, a new method with high localization precision and numerical stability is proposed when the measurements from multiple disjoint sources are subject to the same station position and velocity displacement. According to the available measurements including the angle-of-arrival(AOA), time-of-arrival(TOA), and frequency-of-arrival(FOA), the corresponding pseudo linear equations are deduced. Based on this, a structural total least squares(STLS) optimization model is developed and the inverse iteration algorithm is used to obtain the stationary target location. The localization performance of the STLS localization algorithm is derived, and it is strictly proved that the theoretical performance of the STLS method is consistent with that of the constrained total least squares method under first-order error analysis, both of which can achieve the Cramér-Rao lower bound accuracy. Simulation results show the validity of the theoretical derivation and superiority of the new algorithm.
文摘The constrained total least squares algorithm for the passive location is presented based on the bearing-only measurements in this paper. By this algorithm the non-linear measurement equations are firstly transformed into linear equations and the effect of the measurement noise on the linear equation coefficients is analyzed, therefore the problem of the passive location can be considered as the problem of constrained total least squares, then the problem is changed into the optimized question without restraint which can be solved by the Newton algorithm, and finally the analysis of the location accuracy is given. The simulation results prove that the new algorithm is effective and practicable.
基金The work of the first author was also supported by Grant MM-707/97 from the National Scientific Research Fund of the Bulgarian Ministry of Education and Science .The work of the second author was partially supported by CNPq,CAPES,FINEP,Fundacao Araucaria
文摘A new method for Total Least Squares (TLS) problems is presented. It differs from previous approaches and is based on the solution of successive Least Squares problems. The method is quite suitable for Structured TLS (STLS) problems. We study mostly the case of Toeplitz matrices in this paper. The numerical tests illustrate that the method converges to the solution fast for Toeplitz STLS problems. Since the method is designed for general TLS problems, other structured problems can be treated similarly.
基金the National Natural Science Foundation of China (Nos. 60772007 and 60672008)China Postdoctoral Sci-ence Foundation (No. 20070410258)
文摘Least squares support vector machines (LS-SVMs) are modified support vector machines (SVMs) that involve equality constraints and work with a least squares cost function, which simplifies the optimization procedure. In this paper, a novel training algorithm based on total least squares (TLS) for an LS-SVM is presented and applied to multifunctional sensor signal reconstruction. For three different nonlinearities of a multifunctional sensor model, the reconstruction accuracies of input signals are 0.001 36%, 0.031 84% and 0.504 80%, respectively. The experimental results demonstrate the higher reliability and accuracy of the proposed method for multifunctional sensor signal reconstruction than the original LS-SVM training algorithm, and verify the feasibility and stability of the proposed method.
基金supported by the National Natural Science Foundation of China(61202490)
文摘A new approach called the robust structured total least squares(RSTLS) algorithm is described for solving location inaccuracy caused by outliers in the single-observer passive location. It is built within the weighted structured total least squares(WSTLS)framework and improved based on the robust estimation theory.Moreover, the improved Danish weight function is proposed according to the robust extremal function of the WSTLS, so that the new algorithm can detect outliers based on residuals and reduce the weights of outliers automatically. Finally, the inverse iteration method is discussed to deal with the RSTLS problem. Simulations show that when outliers appear, the result of the proposed algorithm is still accurate and robust, whereas that of the conventional algorithms is distorted seriously.
基金co-supported by Science and Technology on Avionics Integration Laboratory and the Aeronautical Science Foundation of China(No.20105584004)
文摘In order to solve the bearings-only passive localization problem in the presence of erroneous observer position, a novel algorithm based on double side matrix-restricted total least squares （DSMRTLS） is proposed. First, the aforementioned passive localization problem is transferred to the DSMRTLS problem by deriving a multiplicative structure for both the observation matrix and the observation vector. Second, the corresponding optimization problem of the DSMRTLS problem without constraint is derived, which can be approximated as the generalized Rayleigh quotient minimization problem. Then, the localization solution which is globally optimal and asymptotically unbiased can be got by generalized eigenvalue decomposition. Simulation results verify the rationality of the approximation and the good performance of the proposed algorithm compared with several typical algorithms.
文摘The interrelationship between the ARMA model parameters of a vented-box loud-speaker system and lowthequency characteristic parameters is analysed in this paper. These AANA model parameters are deterndned by the total least squares (TLS) method. Therefore, we can measure in the timedomain the fow frequency characteristic parameters of a vented-box loudspeaker system, its impendance curvs and low-frquency response curves. These measured results show a satisfactory agreement with the values obtained by the frequency-domain measurement.
文摘超声医学成像因非侵入式、成本低且实时性好而被广泛应用。超声波动方程具有严重的离散不适定问题,迭代化与正则化结合可以解决这一问题,但重建图像质量较差。为解决这一问题,本文在矩量法基础上,引入Tikhonov-Gaussian方法中的滤波因子,用于校正较小奇异值,将复杂的不适定问题转化为容易求解的最小二乘问题,重构出高质量图像。通过实验数据分析,改进后的截断完全最小二乘算法(truncated total least squares,TTLS)重建质量更高,效果更好,相对误差降低了1.16201%,峰值信噪比提高了0.29132%,信噪比提高了3.0269%,结构相似性提高了1.72531%,图像对比度提高了14.21319%。
基金National Natural Science Foundation of China(Grant No.52107229)the Fund of Robot Technology Used for Special Environment Key Laboratory of Sichuan Province(Grant No.20KFKT02)。
文摘Online parameter identification is essential for the accuracy of the battery equivalent circuit model(ECM).The traditional recursive least squares(RLS)method is easily biased with the noise disturbances from sensors,which degrades the modeling accuracy in practice.Meanwhile,the recursive total least squares(RTLS)method can deal with the noise interferences,but the parameter slowly converges to the reference with initial value uncertainty.To alleviate the above issues,this paper proposes a co-estimation framework utilizing the advantages of RLS and RTLS for a higher parameter identification performance of the battery ECM.RLS converges quickly by updating the parameters along the gradient of the cost function.RTLS is applied to attenuate the noise effect once the parameters have converged.Both simulation and experimental results prove that the proposed method has good accuracy,a fast convergence rate,and also robustness against noise corruption.
基金supported by the National Security Major Basic Research Project of China (973-61334).
文摘Because the real input acceleration cannot be obtained during the error model identification of inertial navigation platform, both the input and output data contain noises. In this case, the conventional regression model and the least squares （LS） method will result in bias. Based on the models of inertial navigation platform error and observation error, the errors-in-variables （EV） model and the total least squares （TLS） method axe proposed to identify the error model of the inertial navigation platform. The estimation precision is improved and the result is better than the conventional regression model based LS method. The simulation results illustrate the effectiveness of the proposed method.
基金supported by the National Natural Science Foundation of China(41074017)
文摘The Gauss-Markov （GM） model and the Errors-in-Variables （EIV） model are frequently used to perform 3D coordinate transformations in geodesy and engineering surveys. In these applications, because the observation errors in original coordinates system are also taken into account, the latter is more accurate and reasonable than the former. Although the Weighted Total Least Squares （WTLS） technique has been intro- duced into coordinate transformations as the measured points are heteroscedastic and correlated, the Variance- Covariance Matrix （VCM） of observations is restricted by a particular structure, namely, only the correlations of each points are taken into account. Because the 3D datum transformation with large rotation angle is a non- linear problem, the WTLS is no longer suitable in this ease. In this contribution, we suggested the nonlinear WTLS adjustments with equality constraints （NWTLS-EC） for 3D datum transformation with large rotation an- gle, which removed the particular structure restriction on the VCM. The Least Squares adjustment with Equality （LSE） constraints is employed to solve NWTLS-EC as the nonlinear model has been linearized, and an iterative algorithm is proposed with the LSE solution. A simulation study of 3D datum transformation with large rotation angle is given to insight into the feasibility of our algorithm at last.
基金The Marine Public Welfare Project of China under contract No.201305032
文摘Significant wave height（SWH） can be computed from the returning waveform of radar altimeter, this parameter is only raw estimates if it does not calibrate. But accurate calibration is important for all applications, especially for climate studies. HY-2a altimeter has been operational since April 2012 and its products are available to the scientific community. In this work, SWH data from HY-2A altimeters are calibrated against in situ buoy data from the National Data Buoy Center（NDBC）, Distinguished from previous calibration studies which generally regarded buoy data as ＂truth＂, the work of calibration for HY-2A altimeter wave data against in situ buoys was applied a more sophisticated statistical technique-the total least squares（TLS） method which can take into account errors in both variables. We present calibration results for HY-2A radar altimeter measurement of wave height against NDBC buoys. In addition, cross-calibration for HY-2A and Jason-2 wave data are talked over and the result is given.
文摘This paper presents a new method of improving Global Positioning System(GPS)positioning precision. Based on the altitude hold mode, the method does not need any other equipment. Under this constraint condition, the Total Least Squares(TLS) algorithm is used to prove that the method is effective. Theoretical analysis shows that the algorithm can significantly improve the GPS positioning precision.
文摘Coordinates are basic needs for both geospatial and non-geospatial professionals and as a result, geodesists have the responsibility to develop methods that are applicable and practicable for determining cartesian coordinates either through transformation, conversion or prediction for the geo-scientific community. It is therefore necessary to implement mechanisms and systems that can be employed to predict coordinates in either two dimensional （2D） or three dimensional （3D） spaces. Artificial Intelligence （AI） techniques and conventional methods within the last decade have been proposed as an effective tool for modeling and forecasting in various scientific disciplines for solving majority of problems. The primary objective of this work is to compare the efficiency of artificial intelligence technique （Feed Forward Back propagation Neural Network （FFBPNN）） and conventional methods （Ordinary Least Squares （OLS）, General Least Squares （GLS）, and Total Least Squares （TLS）） in cartesian planimetric coordinate＇s prediction. In addition, a hybrid approach of conventional and artificial intelligence method thus, TLS-FFBPNN has been proposed in this study for 2D cartesian coordinates prediction. It was observed from the results obtained that FFBPNN performed significantly better than the conventional methods. However, the TLS-FFBPNN when compared with FFBPNN, OLS, GLS and TLS gave stronger and better performance and superior predictions. To further confirm the superiority of the TLS-FFBPNN the Bayesian Information Criterion was introduced. The BIC selected the TLS-FFBPNN as the optimum model for prediction.
基金supported by the National Natural Science Foundation of China, Project No. 42174045under National Key Research and Development Program of China, Project No.2020YFB0505805the National Natural Science Foundation of China, Project No. 41874012。
文摘The reliability theory has been an important element of the classical geodetic adjustment theory and methods in the linear Gauss-Markov model. Although errors-in-variables(EIV) models have been intensively investigated, little has been done about reliability theory for EIV models. This paper first investigates the effect of a random coefficient matrix A on the conventional geodetic reliability measures as if the coefficient matrix were deterministic. The effects of such geodetic internal and external reliability measures due to the randomness of the coefficient matrix are worked out, which are shown to depend not only on the noise level of the random elements of A but also on the values of parameters. An alternative, linear approximate reliability theory is accordingly developed for use in EIV models. Both the EIV-affected reliability measures and the corresponding linear approximate measures fully account for the random errors of both the coefficient matrix and the observations, though formulated in a slightly different way. Numerical experiments have been carried to demonstrate the effects of errors-in-variables on reliability measures and compared with the conventional Baarda's reliability measures. The simulations have confirmed our theoretical results that the EIV-reliability measures depend on both the noise level of A and the parameter values. The larger the noise level of A, the larger the EIV-affected internal and external reliability measures;the larger the parameters,the larger the EIV-affected internal and external reliability measures.
基金supported by National Basic Research Program of China(Grant No.2012CB957703)the National Natural Science Foundation of China(Grant Nos.41074018 and 41104002)
文摘With extensive applications of space geodesy, three-dimensional datum transformation model has been necessarily used to transform the coordinates in the different coordinate systems.Its essence is to predict the coordinates of non-common points in the second coordinate system based on their coordinates in the first coordinate system and the coordinates of common points in two coordinate systems.Traditionally, the computation of seven transformation parameters and the transformation of noncommon points are individually implemented, in which the errors of coordinates are taken into account only in the second system although the coordinates in both two systems are inevitably contaminated by the random errors.Moreover, the coordinate errors of non-common points are disregarded when they are transformed using the solved transformation parameters.Here we propose the seamless (rigorous) datum transformation model to compute the transformation parameters and transform the non-common points integratively, considering the errors of all coordinates in both coordinate systems.As a result, a nonlinear coordinate transformation model is formulated.Based on the Gauss-Newton algorithm and the numerical characteristics of transformation parameters, two linear versions of the established nonlinear model are individually derived.Then the least-squares collocation (prediction) method is employed to trivially solve these linear models.Finally, the simulation experiment is carried out to demonstrate the performance and benefits of the presented method.The results show that the presented method can significantly improve the precision of the coordinate transformation, especially when the non-common points are strongly correlated with the common points used to compute the transformation parameters.
