1995
DOI: 10.1109/78.388853
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Steering vector estimation in uncalibrated arrays

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Cited by 34 publications
(5 citation statements)
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“…. , e jωL sin θr T , (26) where ω l = −2πd l /λ, d l denotes the location of the lth sensor, l = 2…L. In fact, a near-field reference source is also suitable, since all its parameters (including θ r , r r ) are known in advance.…”
Section: The Proposed Techniquementioning
confidence: 99%
See 1 more Smart Citation
“…. , e jωL sin θr T , (26) where ω l = −2πd l /λ, d l denotes the location of the lth sensor, l = 2…L. In fact, a near-field reference source is also suitable, since all its parameters (including θ r , r r ) are known in advance.…”
Section: The Proposed Techniquementioning
confidence: 99%
“…The joint sparsityiteration-based calibration method or gain-phase errors, array geometry errors and mutual coupling is proposed in [25]. The steering vector estimation and robust beamforming techniques for calibrating gain-phase errors, based on estimating the parameters needed (such as power, angle and range parameters) and eliminating the effects of model errors simultaneously, are investigated in [26][27][28]. The bi-affine modeling based method for calibrating array geometry errors and the MUSIC-based method for calibrating mutual coupling are proposed in [29] and [30], respectively.…”
Section: Introductionmentioning
confidence: 99%
“…If the training data contains the SOI component, then even small estimation error in the signal steering vector (SV) and/or array covariance matrix can lead to severe performance degradation [2]. In practice, factors such as inaccurate signal model [3], direction of arrival (DOA) estimation error [4], array perturbations (including array element position [5] and calibration errors [6]), and a moving target [7] can lead to SV estimation errors. Finite sampling sequence [8] leads to an inaccurate covariance matrix.…”
Section: Introductionmentioning
confidence: 99%
“…However, due to the desired signal component, even small error in the steering vector or covariance matrix can lead to severe performance degradation [3], this phenomenon is often called desired signal cancellation. In practice, many factors can lead to steering vector estimation errors, such as inaccurate signal model [4], direction of arrival (DOA) estimation error [5], array perturbations [6], and calibration errors [7]. Finite sample snapshots [8] lead to an inaccurate data covariance matrix.…”
Section: Introductionmentioning
confidence: 99%