Yang J.,State Key Laboratory of Complex Electromagnetic Environmental Effects on Electronic and Information System |
Yang J.,Electronic Engineering Institute |
Zhen G.,State Key Laboratory of Complex Electromagnetic Environmental Effects on Electronic and Information System |
Yin H.,State Key Laboratory of Complex Electromagnetic Environmental Effects on Electronic and Information System |
And 5 more authors.
Journal of Computational Information Systems | Year: 2013
Ultra Wide-band (UWB) is a novel high-speed wireless communication technology. It is dificult to sample UWB signal directly as its wider band width. Compressed sensing (CS) provides a feasible way with under-sampling. However, existing CS-UWB channel estimation method must be used on the assumption that channel sparsity is known. In order to solve this problem, an UWB channel estimation method based on Bayesian Compressed Sensing (BCS) theory is proposed in this paper. BCS introduces Sparse Bayesian Learning (SBL) theory into CS. It sets posterior probability density function controlled by hyperparameters to each value of the object vector. In updating process, hyperparameters corresponding to insignificant multi-paths tends to infinity and their posterior probability tends to zero. This method can distinguish and reconstruct significant multi-paths in channel vector automatically. Experiment result shows that the proposed method can reconstruct original UWB channel effectively when channel sparsity is unknown. © 2013 Binary Information Press.