In set membership estimation, conditional problems arise when the estimate must belong to a given set of assigned structure. Conditional projection algorithms provide estimates that are suboptimal in. terms of the worst-case estimation error. In order to precisely evaluate the suboptimality level of these estimators, tight upper bounds on the estimation errors must be computed as a function of the conditional radius of information, which represents the minimum achievable error. In this paper, tight bounds are derived for l(infinity) and l(1) estimation errors, in a general setting which allows to consider any compact set of feasible problem elements and linearly parameterized estimates.
Garulli, A. (1999). Tight error bounds for projection algorithms in conditional set membership estimation. SYSTEMS & CONTROL LETTERS, 37(5), 293-300 [10.1016/S0167-6911(99)00034-1].
Tight error bounds for projection algorithms in conditional set membership estimation
Garulli, Andrea
1999-01-01
Abstract
In set membership estimation, conditional problems arise when the estimate must belong to a given set of assigned structure. Conditional projection algorithms provide estimates that are suboptimal in. terms of the worst-case estimation error. In order to precisely evaluate the suboptimality level of these estimators, tight upper bounds on the estimation errors must be computed as a function of the conditional radius of information, which represents the minimum achievable error. In this paper, tight bounds are derived for l(infinity) and l(1) estimation errors, in a general setting which allows to consider any compact set of feasible problem elements and linearly parameterized estimates.
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https://hdl.handle.net/11365/28420
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