A fast fixed-point algorithm for independent component analysis
Neural Computation
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
A Noise-Robust EASI Algorithm for Noisy Blind Interference-Signal Separation
CYBERC '11 Proceedings of the 2011 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery
Blind Signal Processing: Theory and Practice
Blind Signal Processing: Theory and Practice
Equivariant adaptive source separation
IEEE Transactions on Signal Processing
Hi-index | 0.00 |
Aiming at the blind signal-jamming separation (BSJS) in wireless communication environment, we propose a noisy BSJS based on Variational Bayesian Independent Component Analysis algorithm to separate the communication signal from jamming signals and noises. This algorithm takes the Kullback---Leibler divergence between the true post distributions of source signals and the approximate ones as objective function, models sources using mixture of Gaussians, and updates parameters of the model using variational-Bayesian learning method, so as to make the estimated approximate posterior distributions close to the true ones and recover source communication signals finally. The simulation results show that the proposed algorithm is effective for the BSJS in noisy environment.