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Further results and insights on subspace based sinusoidal frequency estimation

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3 Author(s)
M. Kristensson ; Nokia Networks, Kista, Sweden ; M. Jansson ; B. Ottersten

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

Published in:

IEEE Transactions on Signal Processing  (Volume:49 ,  Issue: 12 )