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The Effect of Sensor Health on State Estimation |
Abstract |
In this paper, we consider the problem of … 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. lations are provided to assist the theory. +
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Authors | Ling Shi, Michael Epstein, Richard M. Murray + |
ID | 2006h + |
Source | 2006 Conference on Decision and Control + |
Tag | sem06-cdc + |
Title | The Effect of Sensor Health on State Estimation + |
Type | Conference Paper + |
Categories | Papers |
Modification date This property is a special property in this wiki.
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15 May 2016 06:17:37 + |
URL This property is a special property in this wiki.
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http://www.cds.caltech.edu/~murray/preprints/sem06-cdc.pdf + |
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The Effect of Sensor Health on State Estimation + | Title |
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