Robust adaptive beamforming via a novel subspace method for interference covariance matrix reconstruction

Yuan, Xiaolei; Gan, Lu

doi:10.1016/j.sigpro.2016.07.008

Cited by 93 publications

(82 citation statements)

References 22 publications

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“…For each simulation point, 50 Monte Carlo runs are performed. For performance comparison, since the proposed algorithm is an improved version of the INCM-based subspace projection (SP) (INCM-SP) beamformer [16], we will compare with it in detail. Besides, the conventional eigenspace (CE) beamformer [33], the worst case (WC) beamformer [5], the INCM-based quadratically constrained quadratic programming (INCM-QCQP) beamformer [11], the interference cancellation (IC) beamformer [13], and the correlation reconstruction (CR) beamformer [15] are also presented.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…where the iterative start point is the assumed SV ofã j , h is the iteration index, P 1 = P c1 P H c1 , and P 2 = VsV H s . When h → ∞ ,ã j,h will converge to the actual SV of the jth signal [13] [15] [16]. By using the results in [31], we can prove that the maximum eigenvalue of P 1 P 2 is unity, i.e.,…”

Section: Coprime Array-based Steering Vector Estimationmentioning

confidence: 99%

“…In [14] [15], the maximum correlation principle is used to reconstruct the INCM, but the performance degrades when the input SNR is closed to the input interferencenoise ratio (INR). The approach in [16] makes use of the a priori information about the angular sections of the signals and combines projection methods to obtain a more precise estimation of the INCM , but it has performance degradation with the increase of the interferer numbers.…”

Section: Introductionmentioning

confidence: 99%

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Coprime array‐based robust beamforming using covariance matrix reconstruction technique

Liu

Zhang

2018

IET Communications

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A novel robust adaptive beamforming algorithm based on coprime array is proposed in this paper. First, we exploit the virtual array structure of coprime coarray to construct two subspaces. The first subspace is obtained from the covariance matrix of the virtual uniform linear array, whereas the second one is obtained by integrating spatial spectrum over each angular sector of signal. Then, by using a closed-form expression of projection-based approach, the steering vectors of signals are estimated from the intersection of the two subspaces. Moreover, according to the covariance fitting theory, the power associated with the desired signal, interference signals and noise is obtained from the virtual sample covariance matrix. In addition, to reconstruct interference-plus-noise covariance matrix (INCM), the maximum correlation principle is used to transfer the virtual SVs of signals to real ones corresponding to a physical array. Finally, with the estimated the SV of the desired signal and the INCM, the proposed robust algorithm is devised. Simulation results demonstrate the superiority and effectiveness of the proposed algorithm.

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Section: Simulation Resultsmentioning

confidence: 99%

Section: Coprime Array-based Steering Vector Estimationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Coprime array‐based robust beamforming using covariance matrix reconstruction technique

Liu

Zhang

2018

IET Communications

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“…(3) Estimate the desired-signal's DOA in (21) as the angle which maximises the output power according to (17). (4) Estimate the steering vector of the desired signal by using (25).…”

Section: Desired-signal Steering Vector Estimationmentioning

confidence: 99%

Adaptive beamforming based on theoretical interference‐plus‐noise covariance and direction‐of‐arrival estimation

Mohammadzadeh

Kükrer

2018

IET signal process.

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A simple and effective adaptive beamforming technique is proposed for uniform linear arrays, which are based on projection processing for covariance matrix construction and desired-signal steering vector estimation. The optimal minimum variance distortion-less response beamformer is closely achieved through approximating the interference-plus-noise covariance matrix by utilising the eigenvalue decomposition of the received signal's covariance matrix. Moreover, the direction-of-arrival (DOA) of the desired signal is estimated by maximising the beamformer output power in a certain angular sector. In particular, the proposed beamformer utilises the aforementioned DOA in order to estimate the desired-signal's steering vector for general steering vector mismatches. Simulation results indicate the better performance of the proposed method in the presence of the different errors compared with some of the recently introduced techniques.

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“…That being said, diagonal loading is not the unique possible way to achieve robustness and many different approaches have been proposed in the literature. One of them is based on producing a better estimate of the SOI steering vector or/and a better estimate of the interference plus noise covariance matrix, see [15][16][17][18][19] for examples. Imposing additional constraints [20][21][22][23][24][25] or adopting a Bayesian perspective [26,27] to take into account steering vector errors also produces effective methods.…”

Section: Problem Statementmentioning

confidence: 99%

An alternative to diagonal loading for implementation of a white noise array gain constrained robust beamformer

Besson

2018

Signal Processing

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Diagonal loading is one of the most popular methods of robust adaptive beamforming, and the solution to many different problems aimed at producing beamformers which are robust to finite samples effects or/and steering vector errors. Among the latter, constraining the white noise array gain (WNAG) is a meaningful approach. However, relating the loading level to the desired WNAG is not straightforward. In this communication, using a generalized sidelobe canceler structure of the beamformer, we prove that the WNAG constraint can be encoded directly in the beamformer, and the latter can be obtained in a rather simple way from a specific eigenvector and without going through the diagonal loading step.

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Robust adaptive beamforming via a novel subspace method for interference covariance matrix reconstruction

Cited by 93 publications

References 22 publications

Coprime array‐based robust beamforming using covariance matrix reconstruction technique

Coprime array‐based robust beamforming using covariance matrix reconstruction technique

Adaptive beamforming based on theoretical interference‐plus‐noise covariance and direction‐of‐arrival estimation

An alternative to diagonal loading for implementation of a white noise array gain constrained robust beamformer

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