We put forward a robust method for estimating motion parameters from the 3-D space position vectors of feature points on the basis of the modified least median of squares (LMedS) regression estimator. First, initial v...We put forward a robust method for estimating motion parameters from the 3-D space position vectors of feature points on the basis of the modified least median of squares (LMedS) regression estimator. First, initial values of motion parameters are estimated by the primary LMedS, then the motion parameters with an iterative reweighted estimator are re-estimated, in which a hybrid weight function of Huber weight and Tukey weight takes place of the dichotomy weight in the primary LMedS, The algorithm alleviates the difficulty of deleting some outliers while SNR is too low, so we can get a more accurate estimation. Computer simulations show that its performance is satisfactory.展开更多
The least trimmed squares estimator (LTS) is a well known robust estimator in terms of protecting the estimate from the outliers. Its high computational complexity is however a problem in practice. We show that the LT...The least trimmed squares estimator (LTS) is a well known robust estimator in terms of protecting the estimate from the outliers. Its high computational complexity is however a problem in practice. We show that the LTS estimate can be obtained by a simple algorithm with the complexity 0( N In N) for large N, where N is the number of measurements. We also show that though the LTS is robust in terms of the outliers, it is sensitive to the inliers. The concept of the inliers is introduced. Moreover, the Generalized Least Trimmed Squares estimator (GLTS) together with its solution are presented that reduces the effect of both the outliers and the inliers. Keywords Least squares - Least trimmed squares - Outliers - System identification - Parameter estimation - Robust parameter estimation This work was supported in part by NSF ECS — 9710297 and ECS — 0098181.展开更多
基金the High Technology Research and Development Programme of China
文摘We put forward a robust method for estimating motion parameters from the 3-D space position vectors of feature points on the basis of the modified least median of squares (LMedS) regression estimator. First, initial values of motion parameters are estimated by the primary LMedS, then the motion parameters with an iterative reweighted estimator are re-estimated, in which a hybrid weight function of Huber weight and Tukey weight takes place of the dichotomy weight in the primary LMedS, The algorithm alleviates the difficulty of deleting some outliers while SNR is too low, so we can get a more accurate estimation. Computer simulations show that its performance is satisfactory.
文摘The least trimmed squares estimator (LTS) is a well known robust estimator in terms of protecting the estimate from the outliers. Its high computational complexity is however a problem in practice. We show that the LTS estimate can be obtained by a simple algorithm with the complexity 0( N In N) for large N, where N is the number of measurements. We also show that though the LTS is robust in terms of the outliers, it is sensitive to the inliers. The concept of the inliers is introduced. Moreover, the Generalized Least Trimmed Squares estimator (GLTS) together with its solution are presented that reduces the effect of both the outliers and the inliers. Keywords Least squares - Least trimmed squares - Outliers - System identification - Parameter estimation - Robust parameter estimation This work was supported in part by NSF ECS — 9710297 and ECS — 0098181.
基金Supported by the National Natural Science Foundation (50909084 )the Natural Science Foundation of Fujian Province (2009J05107 )Xiamen University of Technology (YKJ08015R)
