稳健的二级嵌套阵列自适应波束形成算法

doi:10.3969/j.issn.1001-2400.2015.06.006

J4 ›› 2015, Vol. 42 ›› Issue (6): 30-36.doi: 10.3969/j.issn.1001-2400.2015.06.006

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Robust adaptive beamforming with the two level nested array

YANG Jie;LIAO Guisheng;LI Jun

(National Lab of Radar Signal Processing, Xidian University, Xi’an 710071, China)

Received:2014-10-10 Online:2015-12-20 Published:2016-01-25
Contact: YANG Jie E-mail:yangjie_xidian@126.com

Abstract

Abstract:

For the beamforming problem in the two level nested array under the condition of signal model mismatch, this paper proposes a robust adaptive beamforming algorithm based on efficient interference-plus-noise covariance matrix reconstruction and semi-definite programming(SDP). Firstly, by using the diagonal growth-curve(DGC) model of the received signal and the search-free ESPRIT method, we reconstruct the interference-plus-noise covariance matrix of the virtual array precisely; then, the interference-plus-noise covariance matrix and a little prior information are applied to construct the optimization problem in robust adaptive beamforming, which can effectively decrease the performance degradation of the traditional MVDR filter in nonideal signal circumstances; finally, the optimization problem can be approximately expressed as an SDP problem by using the SDP relaxation method, and we can resort to the convex optimization software to solve it. Simulation results demonstrate that the proposed method achieves a higher output SINR under different input SNRs or sampling snapshots circumstances as compared to traditional methods.

Key words: two level nested array, robust adaptive beamforming, interference-plus-noise covariance matrix reconstruction, semi-definite programming(SDP), SDP relaxation

CLC Number:

TN958

YANG Jie;LIAO Guisheng;LI Jun. Robust adaptive beamforming with the two level nested array[J].J4, 2015, 42(6): 30-36.

References

［1］ Cox H, Zeskind R M, Owen M H. Robust Adaptive Beamforming［J］. IEEE Transactions on Acoustics, Speech and Signal Processing, 1987, 35(10): 1365-1376.
［2］ Feldman D D, Griffiths L J. A Projection Approach for Robust Adaptive Beamforming［J］. IEEE Transactions on Signal Processing, 1994, 42(4): 867-876.
［3］ Vorobyov S A, Gershman A B, Luo Z Q. Robust Adaptive Beamforming Using Worst-Case Performance Optimization: a Solution to the Signal Mismatch Problem［J］. IEEE Transactions on Signal Processing, 2003, 51(2): 313-324.
［4］ Hassanien A, Vorobyov S A, Wong K M. Robust Adaptive Beamforming Using Sequential Quadratic Programming: an Iterative Solution to the Mismatch Problem［J］. IEEE Signal Processing Letters, 2008, 15(1): 733-736.
［5］ Vorobyov S A. Principles of Minimum Variance Robust Adaptive Beamforming Design［J］. Signal Processing, 2013, 93(12): 3264-3277.
［6］ Piya P, Vaidyanathan P P. Nested Arrays: a Novel Approach to Array Processing with Enhanced Degrees of Freedom［J］. IEEE Transactions on Signal Processing, 2010, 58(8): 4167-4181.
［7］ Piya P, Vaidyanathan P P. Nested Arrays in Two Dimensions, Part I: Geometrical Considerations［J］. IEEE Transactions on Signal Processing, 2012, 60(9): 4694-4705.
［8］ Xu L Z, Stoica P, Li J. A Diagonal Growth Curve Model and Some Signal-processing Applications［J］. IEEE Transactions on Signal Processing, 2006, 54(9): 3363-3371.
［9］ Gu Y J, Leshem A. Robust Adaptive Beamforming Based on Interference Covariance Matrix Reconstruction and Steering Vector Estimation［J］. IEEE Transactions on Signal Processing, 2012, 60(7): 3881-3885.
［10］ Luo Z Q, Ma W K, Anthony M C, et al. Semidefinite Relaxation of Quadratic Optimization Problems［J］. IEEE Signal Processing Magazine, 2010, 27(3): 20-34.
［11］ Hu Z, Qiu R C, Browning J P, et al. A Novel Single-step Approach for Self-coherent Tomography Using Semidefinite Relaxation［J］. IEEE Geoscience and Remote Sensing Letters, 2014, 11(1): 114-118.

Robust adaptive beamforming with the two level nested array

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