1 research outputs found
Blind Sparse Estimation of Intermittent Sources over Unknown Fading Channels
Radio frequency sources are observed at a fusion center via sensor
measurements made over slow flat-fading channels. The number of sources may be
larger than the number of sensors, but their activity is sparse and
intermittent with bursty transmission patterns. To account for this, sources
are modeled as hidden Markov models with known or unknown parameters. The
problem of blind source estimation in the absence of channel state information
is tackled via a novel algorithm, consisting of a dictionary learning (DL)
stage and a per-source stochastic filtering (PSF) stage. The two stages work in
tandem, with the latter operating on the output produced by the former. Both
stages are designed so as to account for the sparsity and memory of the
sources. To this end, smooth LASSO is integrated with DL, while the
forward-backward algorithm and Expectation Maximization (EM) algorithm are
leveraged for PSF. It is shown that the proposed algorithm can enhance the
detection and the estimation performance of the sources, and that it is robust
to the sparsity level.Comment: to appear on IEEE TV