Rapid Convergence Rate in Adaptive Arrays

Reed, I. S.; Mallett, J.D.; Brennan, L. E.

doi:10.1109/taes.1974.307893

Cited by 1,911 publications

(997 citation statements)

References 5 publications

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“…The SMI method requires K > 2N samples of data to achieve an average loss of less than 3 dB [10] and a matrix inversion operation, inv (), forŵ computation. To enable meaningful comparison to the MIL-MVDR, AV and LMS algorithms, we adopt a moving average (MA) approach to computeR iteratively.…”

Section: The Sample Matrix Inversion Algorithmmentioning

confidence: 99%

“…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%

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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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Section: The Sample Matrix Inversion Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Adaptive Beamforming Algorithms for Cancellation of Multiple Interference Signals

Ong¹

2015

PIER M

View full text Add to dashboard Cite

show abstract

“…One is thatR L is converged (according to [20], if L is large enough, it can be thought true in engineering application); the other is that the moving target is not correlated to the clutter (referring to [11,21], it can be thought so when considering the moving target average effect on different range cells). Because the clutter echoes are random variable, one range cell clutter echo is only once realization of them; the NHD result based on them is a variable too.…”

Section: The Interference Target Detection Algorithm Performance Analmentioning

confidence: 99%

“…It is a candidate technology to improve the detecting and tracking of slow moving targets in difficult clutter and jamming environments [9][10][11][12][13][14][15][16][17][18][19][20][21][22]. STAP performance is determined in part by how closely its interference covariance matrix, typically estimated from adjacent range cells located symmetrically around the test cell, matches the interference statistics of the test range bin.…”

Section: Introductionmentioning

confidence: 99%

Study Stap Algorithm on Interference Target Detect Under Nonhomogenous Environment

Gong¹,

Zhu²

2009

PIER

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Abstract-In conventional statistical STAP algorithms, the existence of interference target in training samples will lead to signal cancellation, resulting in the output SCR falling and the moving target detection performance degrading. The nonhomogeneity detector is an effective way to restrain the outlier, which can improve the covariance matrix estimation by detecting the samples containing outliers and rejecting them, and improve the STAP performance. A new interference target detection algorithm is proposed in this paper, the outlier detection is realized by using the samples' data phase information. Compared with traditional method, the improved algorithm is more sensitive to interfering target with different azimuth and intensity. Simulation results demonstrate the validity of this improved method.

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“…(20), the SMI beamformer (8), the DL-SMI beamformer (10), the eigenspace-based beamformer [13], the Bayesian beamformer [23], the worst-case based robust beamformer [14], and the Bayesian beamformer with order recursive implementation [18]. In addition, the optimal SINR is also shown in all figures.…”

Section: Simulationsmentioning

confidence: 99%

Robust Adaptive Beamforming for Steering Vector Uncertainties Based on Equivalent Doas Method

Gu¹,

Shi²,

Chen³

et al. 2008

PIER

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Abstract-The adaptive beamformers often suffer severe performance degradation when there exist uncertainties in the steering vector of interest. In this paper, we develop a new approach to robust adaptive beamforming in the presence of an unknown signal steering vector. Based on the observed data, we try to estimate an equivalent directionof-arrival (DOA) for each sensor, in which all factors causing the steering vector uncertainties are ascribed to the DOA uncertainty only. The equivalent DOA of each sensor can be estimated one by one with the assumption that the elements of the steering vector are uncorrelated with each other. Using a Bayesian approach, the equivalent DOA estimator of each sensor is a weighted sum of a set of candidate DOA's, which are combined according to the value of the a posteriori probability for each pointing direction. In this way, the signal steering vector and the diagonal loading sample matrix inversion (DL-SMI) version adaptive beamformer can be obtained. Numerical simulations illustrate the robustness of the proposed beamforming algorithm. 278Gu et al.

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Rapid Convergence Rate in Adaptive Arrays

Cited by 1,911 publications

References 5 publications

Adaptive Beamforming Algorithms for Cancellation of Multiple Interference Signals

Adaptive Beamforming Algorithms for Cancellation of Multiple Interference Signals

Study Stap Algorithm on Interference Target Detect Under Nonhomogenous Environment

Robust Adaptive Beamforming for Steering Vector Uncertainties Based on Equivalent Doas Method

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