An improved cyclic beamforming method for signal DOA estimation

Markhi, H. El.; Haibala, M. M. O.; Mrabti, F.; Chargé, Pascal; Zouak, M.

doi:10.1007/s11760-007-0023-6

Cited by 12 publications

(10 citation statements)

References 9 publications

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“…Then, the covariance matrix R Y of Y .n/ can be written as [8,[15][16][17] R Then, Equation (8) can be rewritten as Assume P denotes the summation of R X and R Y , according to Equation (9) / denotes the conjugate matrix.…”

Section: B LImentioning

confidence: 99%

“…/ denotes the conjugate matrix. Then, the covariance matrix R Y of Y .n/ can be written as [8,[15][16][17]]…”

Section: B LImentioning

confidence: 99%

“…To accurately achieve the DOA, the optimum direction angle needs to be found to satisfy Equations (17) or (18). Here, the angle range is limited in Section 3.1.…”

Section: Determining Searching Range For Peaksmentioning

confidence: 99%

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An improved MMUSIC algorithm of direction of arrival estimation

2011

Int J Communication

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An improved algorithm is presented for a signal's direction of arrival estimation to reduce the computational complexity of the existing modified multiple signal classification (MMUSIC) algorithm. In this paper, FFT algorithm is introduced to narrow the searching range of the direction angle. Afterwards, a compromise approach-based SVD and orthogonal-triangular decomposition takes the place of traditional double SVDs. Then, during matrix calculation, the noise subspace is achieved for the signal's direction of arrival. Finally, the system simulation demonstrates the efficiency and reliability of this novel MMUSIC algorithm. Compared with the existing algorithm, MMUSIC effectively saves the computational complexity with the approximation estimation performance.

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Section: B LImentioning

confidence: 99%

“…/ denotes the conjugate matrix. Then, the covariance matrix R Y of Y .n/ can be written as [8,[15][16][17]]…”

Section: B LImentioning

confidence: 99%

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An improved MMUSIC algorithm of direction of arrival estimation

2011

Int J Communication

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“…DOA estimation issues such as the effects of model errors, the resolution of closely spaced sources, and the geometry of antennas have been widely studied in the past, with the sources assumed to be far-field point transmitters or reflectors [1][2][3][4][5]. Indeed, in most scenarios the point-source assumption is correct, but in some cases, as for instance the localization of acoustics [6] [7] or bio-medical [8] sources, ocean waves [9], or for mobile channel communication [10], a spatially distributed model of the sources is more appropriate.…”

Section: Introductionmentioning

confidence: 99%

Robustness of the coherently distributed MUSIC algorithm to the imperfect knowledge of the spatial distribution of the sources

Xiong

Picheral

Marcos

2016

SIViP

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The MUltiple SIgnal Classification (MUSIC) estimator has been widely studied for a long time for its high resolution capabilities in the domain of the directional of arrival (DOA) estimation, with the sources assumed to be point. However, when the actual sources are spatially distributed with angular dispersion, the performance of the conventional MUSIC is degraded. This paper deals with the sensitivity of MUSIC to modeling error due to coherently distributed (CD) sources. A performance analysis of an extended MUSIC taking into account a generalized steering vector based on a CD source model (CD-MUSIC) is first studied. We establish closed-form expressions of the DOA estimation bias and mean square error due to both the model error and the effects of a finite number of snapshots. The aim of this paper is also to determine when the point source assumption is acceptable for standard MUSIC. The analytical results are validated by numerical simulations and discussed in different configurations.

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“…The cyclostationarity concept was first introduced into array signal processing by Gardner. Some methods that exploit cyclostationarity have been proposed later to improve the performances of the conventional methods, such that [2], [3]. However, most of the methods are only effective against narrowband signals.…”

Section: Introductionmentioning

confidence: 99%

Cyclic cross-correlation based two-dimensional direction of arrival estimation algorithm

Zhang

Chen

Lü

et al. 2008

2008 International Conference on Information and Automation

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Many modulated communication signals exhibit a cyclostationarity property, corresponding to the underlying periodicity arising from carrier frequencies or baud rates. By exploiting cyclostationarity, the direction-of-arrival (DOA) estimation can be significantly improved. In this paper, we propose a new two-dimensional (2-D) DOA estimation method that exploits cyclostationarity to enhance the noise suppressing effect. The proposed method is based on the cyclic crosscorrelations of sensor outputs and is asymptotically exact for both narrowband and wideband sources. In addition, the new method does not require any 2-D search or pair matching for 2-D DOA estimation problems. Theoretical analysis and computer simulation simultaneously indicate that the proposed method has a better performance in noise suppressing and signal selectivity.

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An improved cyclic beamforming method for signal DOA estimation

Cited by 12 publications

References 9 publications

An improved MMUSIC algorithm of direction of arrival estimation

An improved MMUSIC algorithm of direction of arrival estimation

Robustness of the coherently distributed MUSIC algorithm to the imperfect knowledge of the spatial distribution of the sources

Cyclic cross-correlation based two-dimensional direction of arrival estimation algorithm

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