A Robust Beamforming Method Based on Sparse Constraint

XIE Hu; FENG Da-zheng; YUAN Ming-dong

doi:10.3969/j.issn.0372-2112.2016.09.036

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A Robust Beamforming Method Based on Sparse Constraint

更新时间：2025-07-16

- A Robust Beamforming Method Based on Sparse Constraint
- Acta Electronica Sinica Vol. 44, Issue 9, Pages: 2276-2281(2016)
- 作者机构：
  
  1. 中国空间技术研究院西安分院西安,陕西,西安,710100
  2. 西安电子科技大学雷达信号处理国家重点实验室,陕西,西安,710071
  3. 中国空间技术研究院西安分院西安,陕西,西安,710100
  4. 西安电子科技大学雷达信号处理国家重点实验室,陕西,西安,710071
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China (No.61271293)
- DOI：10.3969/j.issn.0372-2112.2016.09.036
  CLC： TN957
- Published Online：25 September 2016，
  
  Published：2016
- 稿件说明：
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XIE Hu, FENG Da-zheng, YUAN Ming-dong. A Robust Beamforming Method Based on Sparse Constraint[J]. Acta Electronica Sinica, 2016, 44(9): 2276-2281.
DOI：

XIE Hu, FENG Da-zheng, YUAN Ming-dong. A Robust Beamforming Method Based on Sparse Constraint[J]. Acta Electronica Sinica, 2016, 44(9): 2276-2281. DOI： 10.3969/j.issn.0372-2112.2016.09.036.

摘要

通过分析最优自适应波束形成权矢量的子空间组成，发现最优权仅位于低维的干扰加信号子空间中.一般系统所要抑制的干扰数目远小于系统自由度，因此一旦估计出干扰空间和信号导向矢量，只需求解一个低维的组合矢量即可求得自适应权矢量，同时也极大地降低了计算复杂度.本文首先构造一个完备的干扰加信号子空间（IPSS），然后对组合矢量进行稀疏约束，找到一组列数最小的信号加干扰子空间来构造自适应权.仿真实验验证了所提算法的有效性和稳健性.

Abstract

By analyzing the constitution of the optimal adaptive weight

we find that the optimal adaptive weight only lies in a low-dimension subspace spanned by the desired signal steering vector and the interferences subspace.Generally

the number of interferences designed to suppress is much smaller than that of the array sensors.Consequently

once the interference-plus-signal subspace (IPSS) is estimated

only a low-dimension combination vector is needed to compute

which leads to a reduction of the computation complexity.First

we construct a complete IPSS.And then the sparse constraint is imposed on the combination vector to select the least number of column vectors of the complete IPSS to form the adaptive weight.Simulation results validate the effectiveness and robustness of the proposed algorithm.

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