An adaptive generalized sidelobe canceller with derivative constraints

Buckley, K.M.; Griffiths, L.J.

doi:10.1109/tap.1986.1143832

Cited by 260 publications

(122 citation statements)

References 11 publications

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“…To account for the array steering vector errors, additional linear constraints, including point and derivative constraints, can be imposed to improve the robustness of the Capon beamformer [21][22][23][24]. And they are used to widen and flatten the main beam to provide robustness to mismatch in the array response vector and null constraint that are used to place explicit nulls, or zeros, in certain directions.…”

Section: Signal Model and Lcmp Beamformermentioning

confidence: 99%

Robust LCMP Beamformer With Negative Loading

Liu¹,

Yang²

2012

PIER

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Abstract-The adaptive linearly constrained minimum power (LCMP) beamformer can improve the robustness of the Capon beamformer. And quadratic constraints on the weighting vector of the LCMP beamformer can improve the robustness to pointing errors and to random perturbations in sensor parameters. But how to solve it and how to select the constraint parameters are its key problems. In this paper, the Lagrange multiplier method is proposed to solve the LCMP beamformer under quadratic inequality constraint (QIC). The problem of finding the optimal weight vector is solved, and the choice of the quadratic constraint parameter is analyzed and the selected bound is also given. Since the quadratic equality constraint (QEC) is stronger than the quadratic inequality constraint (QIC), the performance of the QECLCMP beamformer is more robust than that of the QICLCMP beamformer. Therefore, the QECLCMP beamformer is proposed and is solved effectively. Numerical examples attest the correctness and the efficiency of the proposed algorithms. And the results show that the QECLCMP beamformer has the advantage of overcoming the steering vector mismatch, namely the optimal negative loading has the preferable robustness.

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Section: Signal Model and Lcmp Beamformermentioning

confidence: 99%

Robust LCMP Beamformer With Negative Loading

Liu¹,

Yang²

2012

PIER

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“…This algorithm adjusts an array of sensors in real time to respond to a desired signal from a chosen direction while discriminating against noise received from other directions. The concept of adaptive systems working on the principle of minimizing the output noise power under the constrained response to specified directions, is also described in various papers [20][21][22][23][24]. One surprising result obtained by Buckley and his colleagues [24], is the observation that employing derivative constraints by linearly-constrained systems is highly dependent upon the location of the phase reference point of the array.…”

Section: Brief Literature Reviewmentioning

confidence: 99%

Study of beamforming techniques for ultrasound imaging in nondestructive testing

1996

NDT & E International

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“…A large scale of adaptive array is commonly utilized to obtain better resolution and interference cancellation performance, which results to the consequence that the computational load becomes the bottleneck in the implementation of an adaptive beamforming algorithm. To save computational cost, the generalized sidelobe canceller (GSC) is an effective approach generally applied in radar and communication systems where the desired signal is only presented in a fraction of time or the amplitude of the desired signal in the auxiliary array is generally very small [7,8]. The GSC can work as an adaptive beamformer that usually improves the gain of the desired signal by forming a mainlobe toward the direction of arrival (DOA) of the desired signal and in the meanwhile suppresses the interferences by nulling at the DOAs of the interference signals.…”

Section: Introductionmentioning

confidence: 99%

A robust generalized sidelobe canceller via steering vector estimation

Xie

et al. 2016

EURASIP J. Adv. Signal Process.

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In the presence of the direction of arrival (DOA) mismatch, the performance of generalized sidelobe canceller (GSC) may suffer severe degradation due to the gain loss of the desired signal in the main array and cancellation. In this paper, one effective GSC algorithm is proposed to improve the robustness against the DOA mismatch of the desired signal. Firstly, two subspaces, which contain the desired signal's actual steering vectors of the main and auxiliary arrays, can be obtained by using the range information of the angle which the desired signal may come from. By rotating these two subspaces, the desired signal's actual steering vectors of the main and auxiliary arrays can be estimated based on the maximum output power criterion. Then, with the estimates of the steering vectors in the former step, the gain loss of the desired signal in the main array can be alleviated. Moreover, one adaptive weight vector with the ability to block the desired signal in the auxiliary array can be obtained simultaneously, which effectively avoids the signal of interest cancellation consequently. Cycle iterative approach is also applied to guarantee the estimation accuracy of a wide range of angle deviation. Numerical simulations demonstrate the effectiveness and applicability of the proposed method.

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An adaptive generalized sidelobe canceller with derivative constraints

Cited by 260 publications

References 11 publications

Robust LCMP Beamformer With Negative Loading

Robust LCMP Beamformer With Negative Loading

Study of beamforming techniques for ultrasound imaging in nondestructive testing

A robust generalized sidelobe canceller via steering vector estimation

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