Abstract :
Due to the high bandwidth of ultra-wideband (UWB) signals, channel estimation is regarded as a challenging issue involved in the realization of digital UWB receivers. Fortunately, the compressed sensing (CS) theory provides a promising way to solve this problem with much lower sampling rate. In traditional CS-based channel estimation schemes, researchers estimate the whole channel waveform at a time. Usually, the implementation complexity is nontrivial. In this paper, we propose a segmented CS-based channel estimation scheme. Specifically, the channel waveform is estimated in a segment by segment manner. For each segment, both the sparsity of the signal and the required scale of the measurement matrix are relatively lower. As a result, the implementation complexity can be reduced effectively. In order to improve the channel estimation performance in noisy environments, making use of the prior information on the UWB signals, we propose a weighted orthogonal matching pursuit (WOMP) algorithm. In WOMP, a weighted matching progress is performed such that dominating components of the target signal can be recovered more reliably. Simulation results show that good performance can be achieved by using our proposed scheme.
