Further results and insights on subspace based sinusoidal frequency estimation

被引：17

作者：

Kristensson, M ^{[1
]}

Jansson, M

Ottersten, B

机构：

[1] Nokia Networks, Kista, Sweden

[2] Royal Inst Technol, Dept Signals Sensors & Syst, Stockholm, Sweden

来源：

IEEE TRANSACTIONS ON SIGNAL PROCESSING | 2001年 / 49卷 / 12期

关键词：

correlation; eigenvalues and eigenfunctions; frequency estimation; maximum likelihood estimation; multidimensional signal processing; singular value decomposition; spectral analysis;

D O I：

10.1109/78.969505

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

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. is also established.

引用

页码：2962 / 2974

页数：13

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