A new method for estimating the bounds of eigenvalues ispresented. In order to show that the method proposed is as effectiveas Qiu's an undamping spring-mass system with 5 nodes and 5 degrees ofreedom is given. To...A new method for estimating the bounds of eigenvalues ispresented. In order to show that the method proposed is as effectiveas Qiu's an undamping spring-mass system with 5 nodes and 5 degrees ofreedom is given. To illustrate that the present method can beapplied to structures which cannot be treated by non-negativedecomposition, a plane frame with 202 nodes and 357 beam elements isgiven. The results show that the present method is effective forestimating the bounds of eigenvalues and is more common than Qiu's.展开更多
Most of the current incoherent polarimetric decompositions employ coherent models to describe ground scattering;however,this cannot truly reflect the fact especially in natural ground surfaces.This paper proposes a hi...Most of the current incoherent polarimetric decompositions employ coherent models to describe ground scattering;however,this cannot truly reflect the fact especially in natural ground surfaces.This paper proposes a highly adaptive decomposition with incoherent ground scattering models(ADIGSM).In ADIGSM,Neumann’s adaptive model is employed to describe volume scattering,and to explain cross-polarized power in remainder matrix,so that we can obtain orientation angle randomness for both volume scattering and the dominant ground scattering.The computation of volume scattering parameters is strictly constrained for non-negative eigenvalues,while the volume scattering parameters that explain the most cross-polarized power are selected.When applying ADIGSM to NASA’s UAVSAR data,the negative component powers were obtained in quite a few forest pixels.Compared with several newest decompositions,the volume scattering power is obviously lowered,especially in areas dominated by surface scattering or double bounce scattering.The orientation angle randomness of each component is reasonable as well.ADIGSM has potential to be applied in the fields such as PolSAR image classification,land cover mapping,speckle filtering,soil moisture and roughness estimation,etc.展开更多
Theoretical results related to properties of a regularized recursive algorithm for estimation of a high dimensional vector of parameters are presented and proved. The recursive character of the procedure is proposed t...Theoretical results related to properties of a regularized recursive algorithm for estimation of a high dimensional vector of parameters are presented and proved. The recursive character of the procedure is proposed to overcome the difficulties with high dimension of the observation vector in computation of a statistical regularized estimator. As to deal with high dimension of the vector of unknown parameters, the regularization is introduced by specifying a priori non-negative covariance structure for the vector of estimated parameters. Numerical example with Monte-Carlo simulation for a low-dimensional system as well as the state/parameter estimation in a very high dimensional oceanic model is presented to demonstrate the efficiency of the proposed approach.展开更多
基金the National Natural Science Foundation (No.19872028)the Mechanical Technology Development Foundation of China
文摘A new method for estimating the bounds of eigenvalues ispresented. In order to show that the method proposed is as effectiveas Qiu's an undamping spring-mass system with 5 nodes and 5 degrees ofreedom is given. To illustrate that the present method can beapplied to structures which cannot be treated by non-negativedecomposition, a plane frame with 202 nodes and 357 beam elements isgiven. The results show that the present method is effective forestimating the bounds of eigenvalues and is more common than Qiu's.
基金This work was supported by the National Key Basic Research Program of China(973 Program)under grant number 2012 CB719906The authors would like to thank UAVSAR team in Jet Propulsion Laboratory(JPL),NASA for processing and providing UAVSAR data and the reviewers for reviewing this paper.The authors especially thank Dr Naiara Pinto in JPL for her useful suggestions to this paper.
文摘Most of the current incoherent polarimetric decompositions employ coherent models to describe ground scattering;however,this cannot truly reflect the fact especially in natural ground surfaces.This paper proposes a highly adaptive decomposition with incoherent ground scattering models(ADIGSM).In ADIGSM,Neumann’s adaptive model is employed to describe volume scattering,and to explain cross-polarized power in remainder matrix,so that we can obtain orientation angle randomness for both volume scattering and the dominant ground scattering.The computation of volume scattering parameters is strictly constrained for non-negative eigenvalues,while the volume scattering parameters that explain the most cross-polarized power are selected.When applying ADIGSM to NASA’s UAVSAR data,the negative component powers were obtained in quite a few forest pixels.Compared with several newest decompositions,the volume scattering power is obviously lowered,especially in areas dominated by surface scattering or double bounce scattering.The orientation angle randomness of each component is reasonable as well.ADIGSM has potential to be applied in the fields such as PolSAR image classification,land cover mapping,speckle filtering,soil moisture and roughness estimation,etc.
文摘Theoretical results related to properties of a regularized recursive algorithm for estimation of a high dimensional vector of parameters are presented and proved. The recursive character of the procedure is proposed to overcome the difficulties with high dimension of the observation vector in computation of a statistical regularized estimator. As to deal with high dimension of the vector of unknown parameters, the regularization is introduced by specifying a priori non-negative covariance structure for the vector of estimated parameters. Numerical example with Monte-Carlo simulation for a low-dimensional system as well as the state/parameter estimation in a very high dimensional oceanic model is presented to demonstrate the efficiency of the proposed approach.
