摘要:
The performance of sparse signal recovery can
be improved if both sparsity and correlation structure of signals can
be exploited. One typical correlation structure is intra-block
correlation in block sparse signals. To exploit this structure, a
framework, called block sparse Bayesian learning (BSBL) framework, has
been proposed recently. Algorithms derived from this framework showed
promising performance but their speed is not very fast, which limits
their applications. This work derives an efficient algorithm from this
framework, using a marginalized likelihood maximization method. Thus it
can exploit block sparsity and intra-block correlation of signals.
Compared to existing BSBL algorithms, it has close recovery performance
to them, but has much faster speed. Therefore, it is more suitable for
recovering large scale datasets.