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In this paper, we propose robust equalizers based on a minimax mean-squares-error (MSE) scheme for wireless multi-input-multi-output (MIMO) communications subject to time-varying channel uncertainties. We consider channel uncertainties within a neighborhood of the estimated channel matrix formed by placing a bound on the spectral matrix norm of channel(More)
This paper considers a state estimation problem for discrete-time systems with Markov switching parameters. For this, the generalized pseudo-Bayesian second-order-extended Viterbi (GPB2-EV) and the interacting multiple-model-extended Viterbi (IMM-EV) algorithms are presented. The derivations of these new algorithms rely on a nontrival incorporation of some(More)
In this paper, we present a plausible time-of-arrival (TOA)-based localization algorithm for mobile location estimation in urban areas. To achieve this goal, we propose a fuzzy-tuned system framework and a fuzzy-tuned interacting multiple-model (fuzzy-tuned IMM) algorithm. To mitigate the effects of both non-line-of-sight (NLOS) errors and mobility(More)
In this paper, we present a new robust receiver for direct-sequence code division multiple-access (DS-CDMA) communication systems with time-varying non-Gaussian channels. Our receiver is developed based on a robust multiple-model channel tracker together with a robust minimum mean-square error (MMSE) decision feedback equalizer (DFE). The robustness is(More)
In this paper, we utilize the synergy between data aggregation and fuzzy inferences to derive a new intelligent multiple-model-based estimation algorithm for robust urban mobile localization. The location estimation problem is cast into a Markov state transitioned system framework with fuzzy inferences for each base station for modeling the dynamics of a(More)