https://murray.cds.caltech.edu/index.php?action=history&feed=atom&title=Multi-dimensional_state_estimation_in_adversarial_environment 2026-09-06T07:18:50Z Revision history for this page on the wiki MediaWiki 1.44.2 https://murray.cds.caltech.edu/index.php?title=Multi-dimensional_state_estimation_in_adversarial_environment&diff=19635&oldid=prev 2016-05-15T05:39:27Z

<p>htdb2wiki: creating page for 2015a_mm15-ccc.html</p> <p><b>New page</b></p><div>{{HTDB paper<br /> | authors = Yilin Mo and Richard M. Murray<br /> | title = Multi-dimensional state estimation in adversarial environment<br /> | source = Submitted, 2015 Chinese Control Conference (CCC)<br /> | year = 2015<br /> | type = Conference Paper<br /> | funding = iCyPhy<br /> | url = http://www.cds.caltech.edu/~murray/preprints/mm15-ccc_s.pdf<br /> | abstract = <br /> We consider the estimation of a vector state based on m measurements that can be potentially manipulated by an adversary. The attacker is assumed to have limited resources and can only manipulate up to l of the m measurements. However, it can the compromise measurements arbitrarily. The problem is formulated as a minimax optimization, where one seeks to construct an optimal estimator that minimizes the “worst-case” error against all possible manipulations by the attacker and all possible sensor noises. We show that if the system is not observable after removing 2l sensors, then the worst-case error is infinite, regardless of the estimation strategy. If the system remains observable after removing arbitrary set of 2l sensor, we prove that the optimal state estimation can be computed by solving a semidefinite programming problem. A numerical example is provided to illustrate the effectiveness of the proposed state estimator.<br /> | flags = <br /> | filetype = PDF<br /> | filesize = 473K<br /> | tag = mm15-ccc<br /> | id = 2015a<br /> }}</div>

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