Performance Comparison of Subspace and Sparsebased DOA Estimation Algorithms

Direction of arrival estimation using antenna array plays an important role in numerous fields of communication, for example, radar, radio telescope, wireless communication, sonar, seismology, mobile communications. Traditionally, high resolution direction of arrival has been performed by using signal subspace algorithms like Estimation of Signal Parameters via Rotational Invariance Techniques (ESPRIT) and MUltiple Signal Classification (MUSIC). However, these algorithms require sufficient time-snapshots for signal covariance estimation. In addition to that, these algorithms also require signal from different directions to be incoherent. In order to overcome the limitations of these algorithms, Direction of Arrival (DOA) algorithms exploiting sparsity have been studied recently. These algorithms have performed better in the presence of coherent signals and by limiting the amount of data. This paper compares the performance of conventional and sparsity-based direction of arrival estimation algorithms under different scenarios. In particular, the performance of these algorithms is compared by varying Signal to Noise Ratio (SNR), number of incident signals and DOAs. Based on the comparison, the strengths and weaknesses of the algorithms under different scenarios would be identified.