We consider the random field estimation problem with parametric trend in wireless sensor networks where the field can be described by unknown parameters to be estimated. Due to the limited resources, the network selec...We consider the random field estimation problem with parametric trend in wireless sensor networks where the field can be described by unknown parameters to be estimated. Due to the limited resources, the network selects only a subset of the sensors to perform the estimation task with a desired performance under the D-optimal criterion. We propose a greedy sampling scheme to select the sensor nodes according to the information gain of the sensors. A distributed algorithm is also developed by consensus-based incremental sensor node selection through information quality computation for and message exchange among neighboring sensors. Simulation results show the good performance of the proposed algorithms.展开更多
A new point of view of robust statistics based on a geometrical approach is tackled in this paper. Estimation procedures are carried out from a new robust cost function based on a chaining of elementary convex norms. ...A new point of view of robust statistics based on a geometrical approach is tackled in this paper. Estimation procedures are carried out from a new robust cost function based on a chaining of elementary convex norms. This chain is randomly articulated in order to treat more efficiently natural outliers in data-set. Estimated parameters are considered as random fields and each of them, named articulated estimator random field （AERF） is a manifold or stratum of a stratified space with Riemannian geometry properties, From a high level excursion set, a probability distribution model Mata is presented and a system model validation geometric criterion （SYMOVAGEC） for system model structures Msys based on Rieeian scalar curvatures is proposed. Numerical results are drawn in a context of system identification.展开更多
基金supported by the National Natural Science Foundation of China-Key Program (No. 61032001),the National Natural Science Foundation of China (No. 60828006)
文摘We consider the random field estimation problem with parametric trend in wireless sensor networks where the field can be described by unknown parameters to be estimated. Due to the limited resources, the network selects only a subset of the sensors to perform the estimation task with a desired performance under the D-optimal criterion. We propose a greedy sampling scheme to select the sensor nodes according to the information gain of the sensors. A distributed algorithm is also developed by consensus-based incremental sensor node selection through information quality computation for and message exchange among neighboring sensors. Simulation results show the good performance of the proposed algorithms.
文摘A new point of view of robust statistics based on a geometrical approach is tackled in this paper. Estimation procedures are carried out from a new robust cost function based on a chaining of elementary convex norms. This chain is randomly articulated in order to treat more efficiently natural outliers in data-set. Estimated parameters are considered as random fields and each of them, named articulated estimator random field （AERF） is a manifold or stratum of a stratified space with Riemannian geometry properties, From a high level excursion set, a probability distribution model Mata is presented and a system model validation geometric criterion （SYMOVAGEC） for system model structures Msys based on Rieeian scalar curvatures is proposed. Numerical results are drawn in a context of system identification.
