Two-Dimensional Direction-of-Arrival Estimation for More Sources Than Sensors

This paper concerns the problem of two-dimensional (2-D) angle (azimuth and elevation) estimation for uncorrelated far-field sources using the proposed sparse rectangular array of antennas, which is constructed by applying the spatial compression on the uniform rectangular array based on the 2-D sparse ruler concept. The correlation matrix of the sources is estimated and applied as an input for the power spectrum / pseudospec-trum estimation algorithm, i.e., classical beamforming, minimum variance distortionless response (MVDR), and multiple signal classification (MUSIC). The simulation study shows that the estimation algorithms are able to spot the direction-of-arrival (DoA) of the sources, which consists of the azimuth and elevation angles, and where the number of sensors is less than the number of sources. Apart from that, the 2-D DoA estimation process using the MUSIC-based method shows better performance from the perspective of the root mean square error, even in a very low sisnal-te-noise ratio value at the receiver.