Optimal Set-membership Filtering Using an Adaptive Minimax Algorithm with Automatic Bound Tuning
نویسندگان
چکیده
This paper shows that optimal set-membership filtering (SMF) is identical to minimax ( ) filtering, if optimal SMF corresponds to the smallest error bound specification that leads to a non-empty feasible solution. An adaptive minimax filtering algorithm that uses optimal quadratic approximations to the minimax cost function is proposed for adaptive implementation of optimal SMF. The proposed algorithm automatically tunes the error bound in an optimal manner, and can also be used for set-membership identification (SMI) when the noise bounds are not known. Simulation results verify the convergence of the proposed algorithm to the minimax solution.
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