Rank reduction direction-of-arrival estimators with an improved robustness against subarray orientation errors

Elkader, S.A.; Wong, K.M.

doi:10.1109/tsp.2006.872321

Cited by 33 publications

(19 citation statements)

References 14 publications

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“…, M − 1}, as shown in Fig. 1, the received signal can be written in matrix form as [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19] …”

Section: Signal Model and Standard Musicmentioning

confidence: 99%

“…Using the Vandermonde sturcture of a ULA, the root-MUSIC algorithm transforms the MUSIC function into a polynomial and finds signal DOAs by rooting instead of spectral search. Although root-MUSIC has been extended to nonuniform linear arrays (NULAs) [12][13][14][15][16][17], and has an improved computational load and threshold performances as compared to the standard MUSIC [18], the former usually requires to find all the roots of a polynomial whose order is about twice of the number of sensors. Therefore, the computational burden of root-MUSIC is in fact much higher than expected, especially when large numbers of sensors are used (Fig.…”

Section: Introductionmentioning

confidence: 99%

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Spatial aliasing for efficient direction-of-arrival estimation based on steering vector reconstruction

Yan

Cao

Rong

et al. 2016

EURASIP J. Adv. Signal Process.

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A new technique is proposed to reduce the computational complexity of the multiple signal classification (MUSIC) algorithm for direction-of-arrival (DOA) estimate using a uniform linear array (ULA). The steering vector of the ULA is reconstructed as the Kronecker product of two other steering vectors, and a new cost function with spatial aliasing at hand is derived. Thanks to the estimation ambiguity of this spatial aliasing, mirror angles mathematically relating to the true DOAs are generated, based on which the full spectral search involved in the MUSIC algorithm is highly compressed into a limited angular sector accordingly. Further complexity analysis and performance studies are conducted by computer simulations, which demonstrate that the proposed estimator requires an extremely reduced computational burden while it shows a similar accuracy to the standard MUSIC.

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“…, M − 1}, as shown in Fig. 1, the received signal can be written in matrix form as [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19] …”

Section: Signal Model and Standard Musicmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Spatial aliasing for efficient direction-of-arrival estimation based on steering vector reconstruction

Yan

Cao

Rong

et al. 2016

EURASIP J. Adv. Signal Process.

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show abstract

“…This presently proposed algorithm could be related to a class of array calibration algorithms and direction finding algorithms [3,4,5,6,7], that handle an array of ideal subarrays with non-ideal inter-subarray relationships. Amongst these series works, only [5] is noticed that allows small intersubarray mis-orientations, which is nonetheless limited to one dimensional rotation on the azimuthal plane, but not the full trivariate Euler angles for three-dimensional mis-orientation.…”

Section: Literature Review Of Relevant Workmentioning

confidence: 99%

Blind calibration and remedy for mis-oriented velocity-sensor triads

Song

2013

2013 IEEE International Conference on Acoustics, Speech and Signal Processing

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This paper introduces a new blind calibration algorithm as well as a new remedy procedure for velocity-sensor triads' mis-orientations. The word "blind" indicates the proposed method requires no priori knowledge of incident sources' direction-of-arrivals (DOAs), and allows the arrays of velocity-sensor triads to be arbitrarily separated at unknown locations in three-dimensional space. Moreover, the present work is applicable to the arrays of velocity-sensor triads with unknown inter-triad gain and phase deviations. Simulation results verify the efficacy and versatility of this proposed scheme.

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“…Well-designed planar arrays, such as uniform circular array (UCA), can meet these requirements. However, ESPRIT and Root-MUSIC can only work on linear array, and algorithms derived from them for other array forms [11,12,13] can only work on particular array patterns, while only 1 direction parameter can be estimated. The MUSIC algorithm, which can be transplanted to more general forms of planar arrays and provides both azimuth and elevation estimation, seems to be more suitable.…”

Section: Introductionmentioning

confidence: 99%

A Fast DOA Estimation Algorithm Based on Polarization MUSIC

Guo¹,

Mao²,

Li³

et al. 2015

RADIOENGINEERING

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Abstract.A fast DOA estimation algorithm developed from MUSIC, which also benefits from the processing of the signals' polarization information, is presented. Besides performance enhancement in precision and resolution, the proposed algorithm can be exerted on various forms of polarization sensitive arrays, without specific requirement on the array's pattern. Depending on the continuity property of the space spectrum, a huge amount of computation incurred in the calculation of 4-D space spectrum is averted. Performance and computation complexity analysis of the proposed algorithm is discussed and the simulation results are presented. Compared with conventional MUSIC, the proposed algorithm has considerable advantage in aspects of precision and resolution, with a low computation complexity proportional to a conventional 2-D MUSIC.

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Rank reduction direction-of-arrival estimators with an improved robustness against subarray orientation errors

Cited by 33 publications

References 14 publications

Spatial aliasing for efficient direction-of-arrival estimation based on steering vector reconstruction

Spatial aliasing for efficient direction-of-arrival estimation based on steering vector reconstruction

Blind calibration and remedy for mis-oriented velocity-sensor triads

A Fast DOA Estimation Algorithm Based on Polarization MUSIC

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