Accelerating Parallel Jacobi Method for Matrix Eigenvalue Computation in DOA Estimation Algorithm

The calculation of eigenvalues of a matrix is required by many algorithms. Specifically, it is the key technique in subspace-based direction of arrival (DOA) estimation algorithms, e.g., multiple signal classification (MUSIC). The calculation of the eigenvalues therefore directly affects the real-time implementation of DOA estimation approaches. However, the classical Jacobi methods are time-consuming. In literature, a parallel implementation has been adopted to accelerate the calculation of eigenvalues. In this paper, we propose to further decrease the execution time of this parallel method. In particular, each parallel unit of the proposed method uses one coordinate rotation digital computer (CORDIC) period per iteration, while more are required by the traditional counterparts, such that the eigenvalue decomposition of the MUSIC algorithm can be accelerated. In addition, the proposed method is implemented in an FPGA platform. The experimental results show that the proposed method is more computationally efficient.