Abstract
Subspace-based methods for parameter identification have received considerable attention in the literature. Starting with a scalar-valued process, it is well known that subspace-based identification of sinusoidal frequencies is possible if the scalar valued data is windowed to form a low-rank vector-valued process. MUSIC and ESPRIT-like estimators have, for some time, been applied to this vector model. In addition, a statistically attractive Markov-like procedure for this class of methods has been proposed. Herein, the Markov-like procedure is reinvestigated. Several results regarding rank, performance, and structure are given in a compact manner. The large sample equivalence with the approximate maximum likelihood method by Stoica et al. (1988) is also established.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
