Computationally Efficient Direction-of-Arrival Estimation Based on Partial A Priori Knowledge of Signal Sources
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Computationally Efficient Direction-of-Arrival Estimation Based on Partial A Priori Knowledge of Signal Sources Lei Huang,1, 2 Shunjun Wu,1 Dazheng Feng,1 and Linrang Zhang1 1 National
Key Laboratory for Radar Signal Processing, Xidian University, 710071 Xi’an, China of Electrical and Computer Engineering, Duke University, Durham, NC 27708-0291, USA
2 Department
Received 19 January 2005; Revised 20 September 2005; Accepted 25 October 2005 Recommended for Publication by Peter Handel A computationally efficient method is proposed for estimating the directions-of-arrival (DOAs) of signals impinging on a uniform linear array (ULA), based on partial a priori knowledge of signal sources. Unlike the classical MUSIC algorithm, the proposed method merely needs the forward recursion of the multistage Wiener filter (MSWF) to find the noise subspace and does not involve an estimate of the array covariance matrix as well as its eigendecomposition. Thereby, the proposed method is computationally efficient. Numerical results are given to illustrate the performance of the proposed method. Copyright © 2006 Hindawi Publishing Corporation. All rights reserved.
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INTRODUCTION
It is desired to estimate the directions-of-arrival (DOAs) of incident signals from noisy data in many areas such as communication, radar, sonar, and geophysical seismology [1]. The classical subspace-based methods, for example, the MUSIC-type [2] algorithms that rely on the decomposition of the observation space into signal subspace and noise subspace, can provide high-resolution DOA estimates with good estimation accuracy. Normally, the classical subspace-based methods are developed without considering any knowledge of the incident signals, except for some general statistical properties like the second-order ergodicity. Nevertheless, the subspace-based methods typically involve the eigendecomposition of the array covariance matrix. As a result, these methods are rather computationally intensive, especially for large arrays. To attain better DOA estimation accuracy and, perhaps, reduce the computational complexity, a number of algorithms that assume some a priori knowledge, such as the waveforms, of the incident signals have been developed in [3–9]. The assumption is reasonable in friendly communications, such as wireless communications and GPS, where certain a priori knowledge of the incident signals is available to the receiver. The a priori information may or may not be explicit. For example, in a packet radio communication system or a mobile communication system, a known preamble may be added to the message for training purposes. In a digital communication system, the modulation
format of the transmitted symbol stream is known to the receiver, although the actual transmitted symbol stream is unknown [10]. With the assumption that the waveforms of the incident signals are known, several computationally efficient maximum likelihood (ML) estimators, for example, the methods named DEML [3], CDEML [4], and WDEML [5] were presented for DOA estimation. Using the
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