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Volume 39 Issue 2
Feb.  2017
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CHEN Pei, ZHAO Yongjun, LIU Chengcheng. Robust Adaptive Beamforming Algorithm for Conformal Arrays Based on Sparse Reconstruction[J]. Journal of Electronics & Information Technology, 2017, 39(2): 301-308. doi: 10.11999/JEIT160436
Citation: CHEN Pei, ZHAO Yongjun, LIU Chengcheng. Robust Adaptive Beamforming Algorithm for Conformal Arrays Based on Sparse Reconstruction[J]. Journal of Electronics & Information Technology, 2017, 39(2): 301-308. doi: 10.11999/JEIT160436

Robust Adaptive Beamforming Algorithm for Conformal Arrays Based on Sparse Reconstruction

doi: 10.11999/JEIT160436
Funds:

The National Natural Science Foundation of China (61401469)

  • Received Date: 2016-04-29
  • Rev Recd Date: 2016-11-10
  • Publish Date: 2017-02-19
  • Adaptive beamforming techniques for conformal arrays suffer from poor universality, difficulty to maintain the main beam and high computational cost. A novel robust adaptive beamforming algorithm for conformal arrays based on sparse reconstruction is proposed to alleviate the existing problems. Firstly, by introducing the Asymptotic Minimum Variance (AMV) criterion, the Interference-Plus-Noise (IPN) covariance matrix reconstruction is realized in a sparse way. Secondly, the Steering Vector (SV) of the Signal Of Interest (SOI) is estimated. Finally, the optimal weight coefficients are achieved. Simulation results demonstrate the effectiveness and robustness of the proposed algorithm and prove that this algorithm can achieve superior output performance over the existing adaptive beamforming methods for conformal arrays in a large range of Signal to Noise Ratio (SNR) of the SOI. Moreover, the proposed algorithm needs fewer snapshots with a lower computational cost and has a faster convergence rate.
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