Particle Filters for Blind FIR Channel Equalization in Non-Gaussian Noise

In this work, we propose new particle filter based blind equalization algorithms for FIR channels subject to additive noise of arbitrary distributions. These algorithms employ artificial evolution methods to jointly generate samples from the missing data and from the unknown channel parameters, which are assumed time-invariant. To achieve these results we introduce a new importance function, which leads to greatly improved performance compared to more obvious alternatives as verified via numerical simulations using Weibull envelope noise processes, in which the performance of the trained MLSE equalizer is approached to a narrow margin