Robust Solutions to Least-Squares Problems with Uncertain Data
SIAM Journal on Matrix Analysis and Applications
Convexity of quadratic transformations and its use in control and optimization
Journal of Optimization Theory and Applications
Convex Optimization
Kalman-Popov-Yakubovich lemma and the S-procedure: A historical essay
Automation and Remote Control
Rejection of bounded exogenous disturbances by the method of invariant ellipsoids
Automation and Remote Control
Multistep specific stochastic inclusions and their multiestimates
Automation and Remote Control
Minimax a posteriori estimation of the Markov processes with finite state spaces
Automation and Remote Control
Restoration of aircraft trajectory from inaccurate measurements
Automation and Remote Control
Robust filtering of process in the stationary difference stochastic system
Automation and Remote Control
On minimax robustness: A general approach and applications
IEEE Transactions on Information Theory
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We consider the minimax estimation problem in a linear observation model under ellipsoidal constraints on the vector of unknown parameters. To solve the problem, we use dual optimization and semidefinite programming methods. The developed algorithms are applied to constructing motion parameter estimates for a maneuvering flying vehicle under constraints on the acceleration vector.