On the adaptive linear estimators, using biased Cramér-Rao bound
Signal Processing
Membership set identification with periodic inputs and orthonormal regressors
Signal Processing - Special section: Multimodal human-computer interfaces
Set-membership proportionate affine projection algorithms
EURASIP Journal on Audio, Speech, and Music Processing
Efficient multichannel NLMS implementation for acoustic echo cancellation
EURASIP Journal on Audio, Speech, and Music Processing
Induced ∞-norm FIR filter for recovering MPSK-type modulus signals
Signal Processing
IEEE Transactions on Signal Processing
Modified quasi-OBE algorithm with improved numerical properties
Signal Processing
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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 identification algorithms known as optimal bounding ellipsoid (OBE) algorithms and their extension to tracking time-varying channels are described. Simulation results show that the OBE channel estimators outperform the least-mean-square (LMS) algorithm and perform comparably with the RLS and the Kalman filter. The concept of set-membership equalization is introduced along with the notion of a feasible equalizer. Necessary and sufficient conditions are derived for the existence of feasible equalizers in the case of linear equalization for a linear FIR additive noise channel. An adaptive OBE algorithm is shown to provide a set of estimated feasible equalizers. The selective update feature of the OBE algorithms is exploited to devise an updator-shared scheme in a multiple channel environment, referred to as updator-shared parallel adaptive equalization (USHAPE). U-SHAPE is shown to reduce the hardware complexity significantly. Procedures to compute the minimum number of updating processors required for a specified quality of service are presented