Direction-of-arrival (DOA) estimation involves the localization of a few sources from a limited number of observations on an array of sensors. Thus, DOA estimation can be formulated as a sparse signal reconstruction problem and solved efficiently with compressive sensing (CS) to achieve highresolution imaging. Utilizing the dual optimal variables of the CS optimization problem, it is shown with Monte Carlo simulations that the DOAs are accurately reconstructed through polynomial rooting (Root-CS). Polynomial rooting is known to improve the resolution in several other DOA estimation methods. However, traditional methods involve the estimation of the cross-spectral matrix hence they require many snapshots and stationary incoherent sources and are suitable only for uniform linear arrays (ULA). Root-CS does not have these limitations as demonstrated on experimental towed array data from ocean acoustic measurements.
|Title of host publication||Proceedings of IEEE International Workshop on Compressed Sensing Theory and its Applications to Radar, Sonar and Remote Sensing|
|Number of pages||5|
|Publication status||Published - 2015|
|Event||2015 IEEE International Workshop on Compressed Sensing Theory and its Applications to Radar, Sonar and Remote Sensing - Pisa, Italy|
Duration: 22 Jun 2015 → 24 Jun 2015
|Conference||2015 IEEE International Workshop on Compressed Sensing Theory and its Applications to Radar, Sonar and Remote Sensing|
|Period||22/06/2015 → 24/06/2015|