Blind Sparse Estimation of Intermittent Sources over Unknown Fading Channels

Document Type

Article

Publication Date

10-1-2019

Abstract

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.

Identifier

85073871094 (Scopus)

Publication Title

IEEE Transactions on Vehicular Technology

External Full Text Location

https://doi.org/10.1109/TVT.2019.2933996

e-ISSN

19399359

ISSN

00189545

First Page

9861

Last Page

9871

Issue

10

Volume

68

Grant

12-D-7248 TO 0046

Fund Ref

Booz Allen Hamilton

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