View source for The Effect of Sensor Health on State Estimation

{{HTDB paper
| authors = Ling Shi, Michael Epstein, Richard M. Murray
| title = The Effect of Sensor Health on State Estimation
| source = 2006 Conference on Decision and Control
| year = 2006
| type = Conference Paper
| funding = NSF/ITR
| url = http://www.cds.caltech.edu/~murray/preprints/sem06-cdc.pdf
| abstract = 
In this paper, we consider the problem of state estimation
using the standard Kalman filter recursions which takes
account of the available sensor health information. Given
a stochastic description of the sensor health, we are able
to show that the expected error covariance converges to a
unique value for all initial values, while the available
previous work only showed the upper bound of the expected
error covariance converges. Our approach provides both
theoretical value to the analysis as well as the potential
to get tighter upper bound. Our results provide a criterion
of evaluating the sensor measurement. In the multisensor
fusion problem, depending on the system error tolerance
levels, it can then be determined whether to fuse a
particular sensor measurement or not. Examples and
simulations are provided to assist the theory.
| flags = 
| tag = sem06-cdc
| id = 2006h
}}