Ultra wideband (UWB) communications involve very sparse channels, since the bandwidth increase results in a better time resolution. This property is used here to propose an efficient algorithm jointly estimating the channel and the transmitted symbols. More precisely, this paper introduces an expectation-maximisation (EM) algorithm within a wavelet domain Bayesian framework for semi-blind channel estimation of multiband orthogonal frequency-division multiplexing (MB-OFDM) based UWB communications. A prior distribution is chosen for the wavelet coefficients of the unknown channel impulse response (CIR) in order to model a sparseness property of the wavelet representation. This prior yields, in maximum a posteriori (MAP) estimation, a thresholding rule within the EM algorithm. We particularly focus on reducing the number of estimated parameters by iteratively discarding 'insignificant' wavelet coefficients from the estimation process. Simulation results using UWB channels issued from both models and measurements show that under sparsity conditions, the proposed algorithm outperforms pilot based channel estimation in terms of mean square error (MSE) and bit error rate (BER). Moreover, the estimation accuracy is improved, while the computational complexity is reduced, when compared to traditional semi-blind methods. Copyright © 2008 John Wiley & Sons, Ltd.