Real-valued propagator method for fast DOA estimation via polynomial rooting

Abstract

In this study, the problem of low-complexity direction-of-arrival (DOA) estimation is addressed, and a novel real-valued propagator method (PM) is presented with a uniform linear array. The covariance matrix is divided into two subarrays and an equivalent noise subspace is obtained by exploiting the standard PM algorithm without eigenvalue decomposition. By a coordinate mapping technique, the complex PM cost function has been converted into a real-valued polynomial whose order only rely on the number of arrays. Using such a mathematical fact, source DOAs can be estimated by polynomial rooting instead of peak searching. The proposed method is able to reduce significant complexity with comparable root-mean-square error performance to the standard PM, which is finally verified by numerical simulations.

Keywords

• mean square error methods
• array signal processing
• covariance matrices
• direction-of-arrival estimation
• polynomials
• eigenvalues and eigenfunctions
• fast doa estimation
• polynomial rooting
• low-complexity direction-of-arrival estimation
• real-valued propagator method
• uniform linear array
• covariance matrix
• equivalent noise subspace
• standard pm algorithm
• eigenvalue decomposition
• coordinate mapping technique
• complex pm cost function
• real-valued polynomial whose order
• source doas
• significant complexity
• comparable root-mean-square error performance