Title
State Estimation for a Class of Non-Uniform Sampling Systems with Missing Measurements.
Abstract
This paper is concerned with the state estimation problem for a class of non-uniform sampling systems with missing measurements where the state is updated uniformly and the measurements are sampled randomly. A new state model is developed to depict the dynamics at the measurement sampling points within a state update period. A non-augmented state estimator dependent on the missing rate is presented by applying an innovation analysis approach. It can provide the state estimates at the state update points and at the measurement sampling points within a state update period. Compared with the augmented method, the proposed algorithm can reduce the computational burden with the increase of the number of measurement samples within a state update period. It can deal with the optimal estimation problem for single and multi-sensor systems in a unified way. To improve the reliability, a distributed suboptimal fusion estimator at the state update points is also given for multi-sensor systems by using the covariance intersection fusion algorithm. The simulation research verifies the effectiveness of the proposed algorithms.
Year
DOI
Venue
2016
10.3390/s16081155
SENSORS
Keywords
Field
DocType
modeling,non-uniform sampling,missing measurement,non-augmented estimator,innovation analysis approach
Data mining,State estimator,Covariance intersection,Algorithm,Optimal estimation,Electronic engineering,State model,Sampling (statistics),Engineering,Estimator,Nonuniform sampling
Journal
Volume
Issue
Citations 
16
8.0
8
PageRank 
References 
Authors
0.54
16
2
Name
Order
Citations
PageRank
Honglei Lin180.54
Shuli Sun273452.41