This paper considers the minimax filtering problem in which the supremum norm of weighted error sequence is minimized. It is shown that the minimax solution is also the optimal Set-Membership Filtering (SMF) solution. An adaptive algorithm is derived that is based on approximating the minimax cost function at each time instant using an optimal quadratic lower bound. The proposed recursions are ...

Set-membership (SM) adaptive filtering is appealing in many practical situations, particularly those with inherent power and computational constraints. The main feature of the SM algorithms is their data-selective coefficient update leading to lower computational complexity and power consumption. The set-membership affine projection (SM-AP) algorithm does not trade convergence speed with misadj...

This paper presents a new filtering approach based on fuzzy-logic which has high performance in mixed noise environments. This filter is mainly based on the idea that each pixel is not allowed to be uniformly fired by each of the fuzzy rules. In the proposed filtering algorithm, the rule membership functions are tuned iteratively in order to preserve the image edges. Several test experiments we...

This paper considers the problems of channel estimation and adaptive equalization in the novel framework of set-membership parameter estimation. Channel estimation using a class of set-membership identiication algorithms known as optimal bounding ellipsoid (OBE) algorithms and their extension to track time-varying channels are described. Simulation results show that the OBE channel estimators o...

Can we solve the filtering problem from the only knowledge of few moments of the noise terms? In this paper, by exploiting set of distributions based filtering, we solve this problem without introducing additional assumptions on the distributions of the noises (e.g., Gaussianity) or on the final form of the estimator (e.g., linear estimator). Given the moments (e.g., mean and variance) of rando...