Proposal of the fast Kernel MUSIC algorithm

Fumie Taga, Hiroshi Shimotahira

    Research output: Contribution to journalArticlepeer-review


    It is an important problem in fields of radar, sonar, and so on to estimate parameters of closely spaced multiple signals. The MUSIC algorithm with the forward-backward (FB) spatial smoothing is considered as the most effective technique at present for the problem with coherent signals in a variety of fields. We have applied this in Laser Microvision. Recently, Shimotahira has proposed the Kernel MUSIC algorithm, which is applicable to cases when signal vectors and noise vectors are orthogonal. It also utilizes Gaussian elimination of the covariance matrix instead of eigenvalue analysis to estimate noise vectors. Although the amount of computation by the Kernel MUSIC algorithm became lighter than that of the conventional MUSIC algorithm, the covariance matrix was formed to estimate noise vectors and also all noise vectors were used to analyze the MUSIC eigenspectrum. The heaviest amount of computation in the Kernel MUSIC algorithm exists in the transformation of the covariance matrix and the analysis of the MUSIC eigenspectrum. We propose a more straightforward algorithm to estimate noise vectors without forming a covariance matrix, easier algorithm to analyze the MUSIC eigenspectrum. The superior characteristics will be demonstrated by results of numerical simulation.
    Original languageEnglish
    Pages (from-to)1232-1239
    Number of pages7
    JournalIEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
    Issue number8
    Publication statusPublished - 1996


    • DOA
    • Fast algorithm
    • Kernel MUSIC
    • MUSIC
    • Spatial smoothing


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