Parallel computing algorithm for MUSIC spatial spectrum estimation

The computing load of the multiple signal classification (MUSIC) algorithm is large, thus it is not suitable for real-time processing. A parallel processing scheme is proposed to solve this problem. The construction of a covariance matrix can be simplified according to its Hermite characteristics; and by the real-value preprocessing, the sequential operations are converted to the field of real numbers. Then the covariance matrix is simplified as a tridiagonal matrix by using Householder transformation, and the eigenvalue and eigenvector of the tridiagonal matrix obtained by QR decomposition are used in spectral peak searching. Finally, the multiprocessor parallel processing technology is fit for each stage of the algorithm. Theoretical analysis and simulation results prove that this method reduces the computing load greatly and increases the speed of processing with little impact on the performance of the algorithm, and it provides a theoretical basis to the efficient realization of the MUSIC algorithm.