A projection approach for robust adaptive beamforming

Feldman, D.D.; Griffiths, L.J.

doi:10.1109/78.285650

Cited by 435 publications

(275 citation statements)

References 11 publications

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“…And we estimate steering vector error in section B just by making use of orthogonality between real steering vector and noise subspace. To mitigate the effect of the residual error as much as possible, we may project the steering vector to signal subspace [25]. The projected vector can be formulated as:…”

Section: A Robust Methods To Mitigate the Effect Of Residual Errormentioning

confidence: 99%

System Error Analysis and Calibration Methods for Multi-Channel Sar

Ma¹,

Li²,

Liao³

2011

PIER

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Abstract-For multi-channel SAR system, since the minimum antenna area constraint is eliminated, wide swath and high resolution SAR image can be achieved. However, compared to mono-channel SAR system, there exist many deleterious factors in multi-channel SAR system which significantly degrade the quality of SAR image. In this paper, all the deleterious factors in the system are analyzed and classified according to their impact on the SAR imaging processing, in addition an new array error estimation method is presented. The validity of the proposed method is verified by experimental results of measured Tri-channel SAR data.

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Section: A Robust Methods To Mitigate the Effect Of Residual Errormentioning

confidence: 99%

System Error Analysis and Calibration Methods for Multi-Channel Sar

Ma¹,

Li²,

Liao³

2011

PIER

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“…We note that (22) is the solution of the eigenspace-based beamformer (ESB) [7,23]. Hence, the proposed method of (19) is similar to the ESB whenλ i , i = 1, .…”

Section: General Formulation Of Fully Data-dependent Loadingmentioning

confidence: 99%

“…, K, are significantly large. The ESB has been widely realized as one of the most powerful robust methods against arbitrary steering vector error [7]. Nevertheless, the proposed method dose not require any information regarding the signal subspace or noise subspace of R xx .…”

Section: General Formulation Of Fully Data-dependent Loadingmentioning

confidence: 99%

“…Moreover, the SMI is very sensitive to the accuracy of the steering vector of the desired signal. It has been shown that even a small mismatch between the presumed steering vector and the actual one causes significant performance degradation [6,7]. Therefore, adaptive beamforming with robustness against the errors mentioned above becomes popular.…”

Section: Introductionmentioning

confidence: 99%

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Robust Adaptive Beamforming Using a Fully Data-Dependent Loading Technique

Lee

2012

PIER B

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Abstract-This paper deals with adaptive array beamforming in the presence of errors due to steering vector mismatch and finite sample effect. Diagonal loading (DL) is one of the widely used techniques for dealing with these errors. However, the main drawback of DL techniques is that there is not an easy and reliable manner to determine the required loading factor. Recently, serval DL approaches proposed the so-called automatic scheme on computing the required loading factor. In this paper, we propose a fully data-dependent loading to overcome the difficulties. The novelty is that the proposed method does not require any additional sophisticated scheme to choose the required loading. The loading factor can be completely obtained from the received array data. Analytical formulas for evaluating the performance of the proposed method under random steering vector error are further derived. Simulation results are provided to confirm the validity of the proposed method and make comparison with the existing DL methods.

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“…(For simplicity, we assume here that the sample covariance matrix is invertible.) If the signal is present in the training data, then it is well known that the performance of the MVDR beamformer with R replaced by R sm of (1.7) degrades considerably [11].…”

Section: Rmentioning

confidence: 99%