A Computationally Efficient Estimation Algorithm for Direction of Arrival in Double Parallel Linear Array

Abstract

The direction of arrival (DOA) is traditionally estimated by subspace algorithms. However, the computation of subspace algorithms is complicated by eigenvalue decomposition (EVD) or singular value decomposition (SVD). To simplify subspace algorithms, this paper proposes a fast one-dimensional (1D) DOA estimation algorithm for double parallel linear array (DPLA). In our algorithm, the equivalent noise subspace is constructed by processing the first column elements of the joint cross-covariance matrix (JCCM), and the DOA is estimated, using the root-multiple signal classification (MUSIC) algorithm. The algorithm effectively simplifies and speeds up the computation by eliminating EVD or SVD. Simulation results confirm that our algorithm can improve the accuracy and reduce the time of DOA estimation. The research results have great application potential in DOA estimation tasks.