Sensitivity analysis of DOA estimation algorithms to sensor errors

Li, F.; Vaccaro, R.J.

doi:10.1109/7.256292

Cited by 62 publications

(28 citation statements)

References 15 publications

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“…Various attempts have been made to investigate both qualitatively and quantitatively the defects of modelling errors on the performance of DF algorithms [1][2][3][4]. Calibration methods [5][6][7] have been developed in order to remedy the resulting degradation in performance, either by estimating the uncertainties present or by eliminating their effect.…”

Section: Introductionmentioning

confidence: 99%

Investigation of array robustness to sensor failure

Alexiou

Manikas

2005

Journal of the Franklin Institute

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Section: Introductionmentioning

confidence: 99%

Investigation of array robustness to sensor failure

Alexiou

Manikas

2005

Journal of the Franklin Institute

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“…This is a widely used disturbance model [8][9][10][11][12] that take into account different sensor errors including those related to calibration. According to Equations (1) and (3), the disturbed data model is,…”

Section: Data Modelmentioning

confidence: 99%

“…In [7], performance comparison between the Time-Frequency MUSIC (TF-MUSIC) and the conventional MUSIC, in presence of additive noise, is provided. In [8], statistical performance analysis in presence of sensor errors without considering the presence of observation noise are conducted for DOA estimation algorithms based on second-order statistics (SOS). In [9], first-order perturbation analysis of the conventional SOS MUSIC and root-MUSIC algorithms is presented.…”

Section: Introductionmentioning

confidence: 99%

Performance analysis for time-frequency MUSIC algorithm in presence of both additive noise and array calibration errors

Khodja

Belouchrani

Abed-Meraim

2012

EURASIP J. Adv. Signal Process.

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This article deals with the application of Spatial Time-Frequency Distribution (STFD) to the direction finding problem using the Multiple Signal Classification (MUSIC)algorithm. A comparative performance analysis is performed for the method under consideration with respect to that using data covariance matrix when the received array signals are subject to calibration errors in a non-stationary environment. An unified analytical expression of the Direction Of Arrival (DOA) error estimation is derived for both methods. Numerical results show the effect of the parameters intervening in the derived expression on the algorithm performance. It is particularly observed that for low Signal to Noise Ratio (SNR) and high Signal to sensor Perturbation Ratio (SPR) the STFD method gives better performance, while for high SNR and for the same SPR both methods give similar performance.

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“…, σ l > 0 and Σ 2 = 0In this subsection we apply the results of Subsects. 2.1-2.2 to a special case[12,13,22]: σ 1 , . .…”

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confidence: 99%

Perturbation analysis of singular subspaces and deflating subspaces

Sun

1996

Numerische Mathematik

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Perturbation expansions for singular subspaces of a matrix and for deflating subspaces of a regular matrix pair are derived by using a technique previously described by the author. The perturbation expansions are then used to derive Fréchet derivatives, condition numbers, and rth-order perturbation bounds for the subspaces. Vaccaro's result on second-order perturbation expansions for a special class of singular subspaces can be obtained from a general result of this paper. Besides, new perturbation bounds for singular subspaces and deflating subspaces are derived by applying a general theorem on solution of a system of nonlinear equations. The results of this paper reveal an important fact: Each singular subspace and each deflating subspace have individual perturbation bounds and individual condition numbers. Mathematics Subject Classification (1991): 65F35 PreliminariesAlthough perturbation bounds for subspaces associated with certain matrix eigenvalue problems and the singular value decomposition of a matrix have been investigated by numerous authors (see, e.g., [1, 4-6, 8-10, 15-18, 21, 23-25]), relatively little attention has been paid to perturbation expansions for the subspaces. Kato [9] gives perturbation series for projections corresponding to invariant subspaces. The author [20] gives perturbation expansions for invariant subspaces. Recently, Vaccaro [22] gives a second-order perturbation expansion for a special class of singular subspaces.

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Sensitivity analysis of DOA estimation algorithms to sensor errors

Cited by 62 publications

References 15 publications

Investigation of array robustness to sensor failure

Investigation of array robustness to sensor failure

Performance analysis for time-frequency MUSIC algorithm in presence of both additive noise and array calibration errors

Perturbation analysis of singular subspaces and deflating subspaces

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