On subspace based sinusoidal frequency estimation

被引：1
|
作者：
Kristensson, M [1 ]
Jansson, M [1 ]
Ottersten, B [1 ]
机构：
[1] Royal Inst Technol, Dept Signals Sensors & Syst, KTH, Stockholm, Sweden
来源：
ICASSP '99: 1999 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, PROCEEDINGS VOLS I-VI | 1999年
关键词：
D O I：
10.1109/ICASSP.1999.756285
中图分类号：
O42 [声学];
学科分类号：
070206 ; 082403 ;
摘要：
Subspace based methods for frequency estimation rely on a low-rank system model that is obtained by collecting the observed scalar valued data samples into vectors. Estimators such as MUSIC and ESPRIT have for some time been applied to this vector model. Also, a statistically attractive Markov-like procedure [1] for this class of methods has been proposed in the literature. Herein, the Markov estimator is re-investigated. Several results regarding rank, performance, and structure are given in a compact manner. The results are used to establish the large sample equivalence of the Markov estimator and the Approximate Maximum Likelihood (AML) algorithm proposed by Stoica et. al..
引用
收藏
页码：1565 / 1568
页数：4
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