TY - GEN
T1 - Pattern coupled sparse Bayesian learning for recovery of time varying sparse signals
AU - Fang, Jun
AU - Shen, Yanning
AU - Li, Hongbin
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2014
Y1 - 2014
N2 - In this paper, we consider the problem of recovering time-varying sparse signals whose sparsity patterns change slowly over time. We develop a new sparse Bayesian learning method for recovery of time-varying sparse signals. A pattern-coupled hierarchical Gaussian prior model is introduced to capture the correlation of the temporal support of time-varying sparse signals. Like the conventional sparse Bayesian learning framework, a set of hyperparameters are introduced to control the sparsity of the signal coefficients. The notable difference is that, for our model, the prior for each coefficient not only involves its own hyperparameter, but also the hyperparameters associated with the coefficients of neighboring temporal observations. In doing this way, sparsity patterns of adjacent (in time) coefficients are coupled through their shared hyperparameters. Hence the prior has the potential to encourage temporally correlated sparsity patterns, while without imposing any pre-defined structures on the recovered signals. Simulation results are provided to illustrate the effectiveness of the proposed algorithm.
AB - In this paper, we consider the problem of recovering time-varying sparse signals whose sparsity patterns change slowly over time. We develop a new sparse Bayesian learning method for recovery of time-varying sparse signals. A pattern-coupled hierarchical Gaussian prior model is introduced to capture the correlation of the temporal support of time-varying sparse signals. Like the conventional sparse Bayesian learning framework, a set of hyperparameters are introduced to control the sparsity of the signal coefficients. The notable difference is that, for our model, the prior for each coefficient not only involves its own hyperparameter, but also the hyperparameters associated with the coefficients of neighboring temporal observations. In doing this way, sparsity patterns of adjacent (in time) coefficients are coupled through their shared hyperparameters. Hence the prior has the potential to encourage temporally correlated sparsity patterns, while without imposing any pre-defined structures on the recovered signals. Simulation results are provided to illustrate the effectiveness of the proposed algorithm.
UR - http://www.scopus.com/inward/record.url?scp=84940768033&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84940768033&partnerID=8YFLogxK
U2 - 10.1109/ICDSP.2014.6900755
DO - 10.1109/ICDSP.2014.6900755
M3 - Conference contribution
AN - SCOPUS:84940768033
T3 - International Conference on Digital Signal Processing, DSP
SP - 705
EP - 709
BT - 2014 19th International Conference on Digital Signal Processing, DSP 2014
T2 - 2014 19th International Conference on Digital Signal Processing, DSP 2014
Y2 - 20 August 2014 through 23 August 2014
ER -