Controlling Adaptive Antenna Arrays with the Sample Matrix Inversion Algorithm

Horowitz, Larry L.; Blatt, Howard; Brodsky, Wesley G.; Senne, Kenneth D.

doi:10.1109/taes.1979.308769

Cited by 40 publications

(12 citation statements)

References 2 publications

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“…The main beamforming algorithms include Least Mean Square (LMS) [18], Sample Matrix Inversion (SMI) [19], and Minimum Variance Distortionless Response (MVDR) [18,20]. In this paper, the proposed array extension technique is combined with MVDR.…”

Section: Beamforming Based On Steering Vector Conjugate Symmetry Arraymentioning

confidence: 99%

Symmetric Extension of Steering Vectors and Beamforming

Ma¹,

Pan²

2018

PIER M

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Aiming at problems that interpolated array has large amount of computation and high sensitivity to transformation angle and interpolated step, a new array extension algorithm which is symmetric extension steering vector is proposed. In this paper, two properties of the conjugate of received data and the source covariance matrix being a real diagonal matrix are exploited to extend the dimensions of the covariance matrix. However, the essence of this extension method is the symmetric extension of the steering vector. The high complexity and degradation of the performance of interpolated array beamforming caused by the sensitivity of angle and interpolated step are improved. Numerical simulations confirm the validity of the proposed algorithm. Compared with existing algorithms, the proposed algorithm is not affected by the angle range of transformation and interpolated step. Besides, the complexity of array extension using this proposed algorithm is much lower than the interpolated transformation method.

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Section: Beamforming Based On Steering Vector Conjugate Symmetry Arraymentioning

confidence: 99%

Symmetric Extension of Steering Vectors and Beamforming

Ma¹,

Pan²

2018

PIER M

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“…According to the ASLC sample matrix inversion (SMI) algorithm [6,7], the output of the ASLC system can be described as…”

Section: Two-dimension Aslc System and Its Dimension Reductionmentioning

confidence: 99%

Performance analysis of two-dimension ASLC system with blocking matrix

Liu

Rao

Qiu

et al. 2010

2010 International Symposium on Intelligent Signal Processing and Communication Systems

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An improved adaptive side lobe cancelling (ASLC) system which can restrain the phenomenon that the desired signal is cancelled is proposed. A blocking matrix is introduced in the auxiliary channel of a traditional ASLC system. And then the jamm ing is estimated by a Wiener filter. At last, the signal of the main channel minus the estimated interference is the output of the system. The results show that the ECCM improvement factor (ElF) of the new ASLC system with blocking matrix is on average 15 dB larger than the ASLC system without it. The feasibility and effectiveness of this new method have been verified

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“…The array weights of the MVDR beamformer can be adapted through various algorithms. For example, the Sample Matrix Inversion (SMI) based algorithm is a fast adaptive beamforming/nulling technique because it directly calculates the covariance matrix [10][11][12]. SMI avoids the problem of eigenvalue spread that often limits the convergence rate for close-loop algorithms such as the Least Mean Square (LMS) approach.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Beamforming Algorithms for Cancellation of Multiple Interference Signals

Ong¹

2015

PIER M

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Abstract-This paper proposes a fast Minimum-Variance-Distortionless-Response (MVDR) beamforming algorithm for an antenna array for cancellation of multiple interference signals. The proposed algorithm uses Sample-Average Estimate (SAE) of the data covariance matrix and reduces its computational effort by applying the Matrix-Inversion-Lemma (MIL) to its covariance Matrix Inversion (MI) operation. The proposed algorithm is compared to two SAE-based algorithms: the Sample Matrix Inversion (SMI) algorithm that requires an MI operation and the Auxiliary Vector (AV) algorithm that does not need an MI operation. A non-SAE based algorithm using the Least Mean Square (LMS) method is also included for comparison. Simulation results show that the proposed algorithm converges slower than the SMI scheme but outperforms the AV and LMS schemes during the transient phase. Once convergence is achieved, the proposed algorithm converges to a better Mean Square Error than the rest of the algorithms evaluated.

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Controlling Adaptive Antenna Arrays with the Sample Matrix Inversion Algorithm

Cited by 40 publications

References 2 publications

Symmetric Extension of Steering Vectors and Beamforming

Symmetric Extension of Steering Vectors and Beamforming

Performance analysis of two-dimension ASLC system with blocking matrix

Adaptive Beamforming Algorithms for Cancellation of Multiple Interference Signals

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