<p>Aiming at the weak signal enhancement, target detection and anti-jamming, and reduction of energy consumption faced by array radar technology in complex electromagnetic environments, this paper proposes a low sidelobe level high-gain adaptive beamforming method for uniform subarrays. The proposed method utilizes the virtual interference signals iteration method to greatly reduce the computational dimension on the basis of ensuring the output performance. Firstly the proposed algorithm compensates the delay of the array elements within the subarray with respect to the reference array element by element-level weighting and derives the array output after element-level weighting. Next the subarray-level interference plus noise covariance matrix was derived. Then the Lagrange method is applied to solve the weight vector for each iteration. Finally, the algorithm is made to iterate until convergence by adding virtual interference signals. The theoretical derivation and simulation experiments demonstrate the effectiveness and reliability of the proposed method.</p>

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The low sidelobe level high-gain adaptive beamforming method for uniform subarrays

  • Yuxi Du,
  • Weijia Cui,
  • Bin Ba,
  • Guanghui Su,
  • Long Zhang

摘要

Aiming at the weak signal enhancement, target detection and anti-jamming, and reduction of energy consumption faced by array radar technology in complex electromagnetic environments, this paper proposes a low sidelobe level high-gain adaptive beamforming method for uniform subarrays. The proposed method utilizes the virtual interference signals iteration method to greatly reduce the computational dimension on the basis of ensuring the output performance. Firstly the proposed algorithm compensates the delay of the array elements within the subarray with respect to the reference array element by element-level weighting and derives the array output after element-level weighting. Next the subarray-level interference plus noise covariance matrix was derived. Then the Lagrange method is applied to solve the weight vector for each iteration. Finally, the algorithm is made to iterate until convergence by adding virtual interference signals. The theoretical derivation and simulation experiments demonstrate the effectiveness and reliability of the proposed method.