Blind sparsity-aware multi-source localization

We tackle the problem of localizing multiple sources in multipath environments using a recently proposed sparsity-aware correlation-based localization paradigm. It is shown in our previous studies that involving cross-correlations leads to a considerable improvement in terms of number of identifiable sources; however, the sources need to have known and similar statistics in order to construct a fingerprinting map and localize them. To surmount this constraint, we approach the problem of sparsity-aware localization from a frequency-domain perspective and propose a method which is blind to the statistics of the source signals. Moreover, we also show how this approach can further improve the performance in terms of number of identifiable sources. Our simulation results corroborate the efficiency of the proposed algorithm in terms of localization accuracy as well as detection capability.