文章摘要
彭加强, 郑桂妹.基于解耦原子范数最小化的二维DOA估计[J].空军工程大学学报:自然科学版,2021,22(1):55-61
基于解耦原子范数最小化的二维DOA估计
A Two Dimensional DOA Estimation Based on Decoupled Atomic Norm Minimization
  
DOI:
中文关键词: 2D-DOA估计  ANM  DANM
英文关键词: two dimensional DOA estimation  ANM  DANM
基金项目:国家自然科学基金(61971438);陕西省自然科学基金(2019JM 155);陕西省青年托举人才项目(20180109)
作者单位
彭加强, 郑桂妹 空军工程大学防空反导学院 西安 710051 
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中文摘要:
      近年来,原子范数最小化算法成为DOA估计领域的重要工具。针对二维DOA估计中解耦原子范数最小化DANM算法只适用于单快拍的场景,提出一种适用于多快拍场景的改进DANM算法。首先,通过改变DANM算法中的优化模型结构,进一步将基于矢量化的传统2D ANM求解模型解耦为2个一维ANM求解模型,使其适用于多快拍的场景;其次,为了避免大快拍所带来的高维度计算,将接收数据及其转置的协方差矩阵分别作为2个维度ANM求解模型的计算数据用于模型求解,使该维度的ANM求解模型维数限定于有限的传感器数目;最后,通过MUSIC算法求出每个维度的DOA,并利用简单的2D配对方法进行配对得到2D DOA估计。数值仿真结果证明该算法保持了ANM类算法的估计性能优势,与DANM算法相比提高了估计精度和稀疏恢复能力,与基于对偶的2D ANM算法相比显著缩短了计算时间。
英文摘要:
      In recent years, the atomic norm minimization (ANM) algorithm has become an important tool in the field of DOA estimation. Aimed at the problem that the DANM is only suitable for single snapshot, an improved decoupled atomic norm minimization algorithm is proposed for both single snapshot and multiple snapshots models. Firstly, through changing the primal optimized model structure in the DANM algorithm, the traditional two dimensional ANM algorithm based on vectorization is further decoupled into two one dimensional ANM solution models to make it suitable for multiple snapshots models. Secondly, in order to avoid the high dimensional calculations brought by the large snapshot, the covariance matrix of the received data and the covariance matrix of the transpose of the received data are used as the calculation data of the two 1D ANM solution model for model solving, so that the size of the dimension of ANM model is limited to the number of sensors. Finally, the DOA of each dimension are obtained by multiple signal classification algorithm, and the 2D DOA estimation is obtained by a simple 2D pairing method. The proposed algorithm maintains the estimation performance advantages of ANM algorithms. Compared with the DANM algorithm, this algorithm improves the estimation accuracy and sparse recovery ability. And compared with the duality based 2D ANM algorithm, the CPU time is significantly shortened. The algorithm is valid.
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