https://murray.cds.caltech.edu/index.php?action=history&feed=atom&title=The_Effect_of_Sensor_Health_on_State_Estimation 2026-07-27T11:49:22Z Revision history for this page on the wiki MediaWiki 1.44.2 https://murray.cds.caltech.edu/index.php?title=The_Effect_of_Sensor_Health_on_State_Estimation&diff=19851&oldid=prev 2016-05-15T06:17:37Z

<p>htdb2wiki: creating page for 2006h_sem06-cdc.html</p> <p><b>New page</b></p><div>{{HTDB paper<br /> | authors = Ling Shi, Michael Epstein, Richard M. Murray<br /> | title = The Effect of Sensor Health on State Estimation<br /> | source = 2006 Conference on Decision and Control<br /> | year = 2006<br /> | type = Conference Paper<br /> | funding = NSF/ITR<br /> | url = http://www.cds.caltech.edu/~murray/preprints/sem06-cdc.pdf<br /> | abstract = <br /> In this paper, we consider the problem of state estimation<br /> using the standard Kalman filter recursions which takes<br /> account of the available sensor health information. Given<br /> a stochastic description of the sensor health, we are able<br /> to show that the expected error covariance converges to a<br /> unique value for all initial values, while the available<br /> previous work only showed the upper bound of the expected<br /> error covariance converges. Our approach provides both<br /> theoretical value to the analysis as well as the potential<br /> to get tighter upper bound. Our results provide a criterion<br /> of evaluating the sensor measurement. In the multisensor<br /> fusion problem, depending on the system error tolerance<br /> levels, it can then be determined whether to fuse a<br /> particular sensor measurement or not. Examples and<br /> simulations are provided to assist the theory.<br /> | flags = <br /> | tag = sem06-cdc<br /> | id = 2006h<br /> }}</div>

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