A New Approach to Achieve a Trade-Off Between Direction-of-Arrival Estimation Performance and Computational Complexity

The multiple signal classification (MUSIC) algorithm is a promising method for the plethora of problems related to the direction-of-arrival (DOA) estimation. Conventionally, this approach uses the whole sensor array observations to obtain the signal or noise subspace, which consequently leads to a huge computational burden. In this letter, to circumvent this problem, we make a significant modification to the traditional MUSIC algorithm. First, we compute only two sub-matrices of the sample covariance matrix (SCM) exploiting the Nystrom method avoiding its complete calculation. These matrices can be used to construct an accurate noise subspace without calculating the SCM and its eigenvalue decomposition (EVD). Furthermore, to have a uniform DOA estimation, we modify the classical ULA by displacing two antenna elements from both the ends of the array to a top and a bottom of the array axis. This unique structure improves the estimation of DOAs near and at the array end fires. Several numerical results are included to confirm the efficacy of the new method.