Performance analysis of direct data domain least squares approach for beamforming

Yilmazer, Nuri; Elufiede, Teniola; Yilmaz, Muhittin; Dobriyal, Mayank; Babiceanu, Radu F.

doi:10.1109/icsmc.2011.6084200

Cited by 1 publication

(13 citation statements)

References 11 publications

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“…

s_{0} = \exp ([], j 2 π \frac{((), n = 0) d}{λ} \cos θ_{s}); n = 0

Similarly, the above result is also true for U (1, 2) − z −1 U (1, 3) and U ( i , j ) − z −1 U ( i , j + 1) per i = 1, 2, …, L + 1 and j = 1, 2, …, L , in which L = M /2. Therefore, a matrix of lower rank from U , that is, T L × ( L + 1), can be formed as follows 2

bold-italicT = {[\begin{array}{c} x_{0} - z^{- 1} x_{1} & x_{1} - z^{- 1} x_{2} & \dots \\ ⋮ & ⋮ & ⋮ \\ x_{M - L - 1} - z^{- 1} x_{M - L} & x_{M - L} - z^{- 1} x_{M - L + 1} & \dots \end{array} \begin{array}{c} x_{L} - z^{- 1} x_{L + 1} \\ ⋮ \\ x_{M - 1} - z^{- 1} x_{M} \end{array}]}_{L \times ((), L + 1)}

In order to restore signal components in the adaptive process, the gain of the sub‐array formed by the L + 1 elements in line with θ s should be proved.…”

Section: An Overview Of the D3ls Methodsmentioning

confidence: 99%

“…The voltage obtained from the mth element of the antenna array sampled at a given time, xm, can be modeled as follows 2 :…”

Section: An Overview Of the D 3 Ls Methodsmentioning

confidence: 99%

“…Therefore, it does not need the covariance matrix and has fewer computational complexity and volume compared to statistical algorithms. This means that the number of these signals should be half of the array elements number in this algorithm in order to null the entering angle of undesirable signals 2 . One of the shortcomings of this method is the high bandwidth of the main lobe and high level of side lobes 8 .…”

Section: Robustness D3ls Beamformingmentioning

confidence: 99%

“…This means that the number of these signals should be half of the array elements number in this algorithm in order to null the entering angle of interference and undesirable signals. 2 The D 3 LS method can remove jammers with large zero depth, but 3-dB bandwidth of its main lobe is high, and it has a high side lobe level.…”

Section: Introductionmentioning

confidence: 99%

“…In statistical beamforming algorithms, processing takes place through collecting a large number of samples from the received signal and estimating covariance matrix. 1,2 One of the problems of statistical methods is the large amount of their calculations. The amount of data required for proper covariance matrix estimate obeys the rule known as the RMB.…”

Section: Introductionmentioning

confidence: 99%

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Robustness D³LS beamforming algorithm against the uncertainties in a uniform linear array radar of dipole antenna

Zarezadeh

Ghorbani

2020

Int J Communication

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In practical terms, direct data domain least squares (D 3 LS) beamforming algorithm performance is severely decreased by the array steering vector uncertainty. In this paper, we investigate the performance of D 3 LS approach for beamforming. Uniform linear antenna array radar of dipole antenna is considered in the presence of mutual coupling between the elements of the array.The performance of D 3 LS beamforming algorithm is severely affected by uncertainty caused by mutual coupling. In order to test the robustness of the D 3 LS approach against uncertainty, a quadratic inequality constraint is considered to optimize weight vector of D 3 LS beamforming algorithm. The convex optimization method is used to calculate optimized weight vector. The proposed convex optimization problem is solved by barrier method. It is a subset of Interior-point numerical analysis methods. Several different scenarios are used to evaluate the proposed algorithm in MATLAB.

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“…

s_{0} = \exp ([], j 2 π \frac{((), n = 0) d}{λ} \cos θ_{s}); n = 0

bold-italicT = {[\begin{array}{c} x_{0} - z^{- 1} x_{1} & x_{1} - z^{- 1} x_{2} & \dots \\ ⋮ & ⋮ & ⋮ \\ x_{M - L - 1} - z^{- 1} x_{M - L} & x_{M - L} - z^{- 1} x_{M - L + 1} & \dots \end{array} \begin{array}{c} x_{L} - z^{- 1} x_{L + 1} \\ ⋮ \\ x_{M - 1} - z^{- 1} x_{M} \end{array}]}_{L \times ((), L + 1)}

In order to restore signal components in the adaptive process, the gain of the sub‐array formed by the L + 1 elements in line with θ s should be proved.…”

Section: An Overview Of the D3ls Methodsmentioning

confidence: 99%

“…The voltage obtained from the mth element of the antenna array sampled at a given time, xm, can be modeled as follows 2 :…”

Section: An Overview Of the D 3 Ls Methodsmentioning

confidence: 99%

Section: Robustness D3ls Beamformingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Robustness D³LS beamforming algorithm against the uncertainties in a uniform linear array radar of dipole antenna

Zarezadeh

Ghorbani

2020

Int J Communication

View full text Add to dashboard Cite

show abstract

Performance analysis of direct data domain least squares approach for beamforming

Cited by 1 publication

References 11 publications

Robustness D³LS beamforming algorithm against the uncertainties in a uniform linear array radar of dipole antenna

Robustness D³LS beamforming algorithm against the uncertainties in a uniform linear array radar of dipole antenna

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Performance analysis of direct data domain least squares approach for beamforming

Cited by 1 publication

References 11 publications

Robustness D3LS beamforming algorithm against the uncertainties in a uniform linear array radar of dipole antenna

Robustness D3LS beamforming algorithm against the uncertainties in a uniform linear array radar of dipole antenna

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Product

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Robustness D³LS beamforming algorithm against the uncertainties in a uniform linear array radar of dipole antenna

Robustness D³LS beamforming algorithm against the uncertainties in a uniform linear array radar of dipole antenna