A Novel Angle Estimator for Signals withKnown Waveforms
نویسندگان
چکیده
A large-sample decoupled maximum likelihood angle estimator for signals with known waveforms is presented by exploiting the a priori knowledge that the observation noise can be modeled as spatially white. We show that incorporating this knowledge improves signiicantly angle estimation accuracy over existing angle estimators for signals with known waveforms. MUSIC, ESPRIT, and MODE, do not assume any knowledge of the incident signals except for some general statistical properties such as the second-order ergodicity (see 1] and the references therein). Recently, there has been a growing interest in developing angle estimators that exploit some a priori knowledge, e.g., the known waveforms, of the incident signals. Such estimators can be used in various applications including wireless communications where known preamble sequences are often transmitted for training purposes. An interesting algorithm in this category is the decoupled maximum likelihood (DEML) angle estimator for signals with known waveforms 2]. It is an eecient large sample maximum likelihood (ML) method. Although DEML was derived only for uncorrelated signals, an extension of DEML, referred to as CDEML, was made in 3] to handle coherent signals. It has been found that these estimators provide signiicantly more accurate angle estimation than conventional estimators including MUSIC, ESPRIT, and MODE. Moreover, while most conventional angle estimators require either the observation noise be spatially white or the spatial noise covariance matrix
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