Structure-Aware Bayesian Compressive Sensing for Frequency-Hopping Spectrum Estimation With Missing Observations - 2018 PROJECT TITLE :Structure-Aware Bayesian Compressive Sensing for Frequency-Hopping Spectrum Estimation With Missing Observations - 2018ABSTRACT:During this Project, we tend to address the matter of spectrum estimation of multiple frequency-hopping (FH) signals within the presence of random missing observations. The signals are analyzed among the bilinear time-frequency (TF) illustration framework, where a TF kernel is intended by exploiting the inherent FH signal structures. The designed kernel permits effective suppression of cross-terms and artifacts because of missing observations whereas preserving the FH signal autoterms. The kerneled results are represented in the instantaneous autocorrelation operate domain, that are then processed using a redesigned structure-aware Bayesian compressive sensing algorithm to accurately estimate the FH signal TF spectrum. The proposed methodology achieves high-resolution FH signal spectrum estimation even when a massive portion of data observations is missing. Simulation results verify the effectiveness of the proposed technique and its superiority over existing techniques. Did you like this research project? To get this research project Guidelines, Training and Code... Click Here facebook twitter google+ linkedin stumble pinterest Stochastic Routing and Scheduling Policies for Energy Harvesting Communication Networks - 2018 Subspace Rejection for Matching Pursuit in the Presence of Unresolved Targets - 2018
