Tight error bounds for projection algorithms in conditional set membership estimation
✍ Scribed by A. Garulli
- Publisher
- Elsevier Science
- Year
- 1999
- Tongue
- English
- Weight
- 112 KB
- Volume
- 37
- Category
- Article
- ISSN
- 0167-6911
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✦ Synopsis
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 ' ∞ and '1 estimation errors, in a general setting which allows to consider any compact set of feasible problem elements and linearly parameterized estimates.