低信噪比下非冗余阵列的无网格DOA估计

波达方向(direction of arrival, DOA)是阵列信号处理模型中的非线性参数,当信噪比较低时,其估计值会偏离真实值。为了降低无网格DOA估计方法中该问题的阈值,介绍了一种基于无网格的基于协方差的稀疏迭代估计(sparse iterative covariance-based estimation, SPICE)方法。引入了最大似然求根多重信号分类(maximum likelihood root multiple signal classification, ML-Root-MUSIC)来计算DOA,使用最大似然准则来选择根,可以降低阈值并获得更好的分辨率特性。在原始无网格SPICE的优化问题中加入了负熵项,使得无网格SPICE的均方根误差曲线更接近于Cramer-Rao下界。最后,蒙特卡罗仿真实验验证了所提方法在低信噪比非冗余阵列情况下的优越性。

Gridless DOA estimation for non-redundant array at low SNR

Direction of arrival (DOA) is a nonlinear parameter in the array signal processing model, and its estimation deviates from the true value when the signal-to-noise ratio is low. In order to lower the threshold of this problem in gridless DOA estimation method, a method based on gridless sparse iterative covariance-based estimation (SPICE) is introduced. A maximum likelihood root multiple signal classification (ML-Root-MUSIC) is introduced to calculate DOA, which uses ML criterion to select roots and can lower the threshold and get better resolution characteristic. A negative entropy term is added to the optimization problem in the original gridless SPICE, so that the root mean square error curve of gridless SPICE is closer to Cramer-Rao lower bound in large-snapshot case. Finally, Monte Carlo simulation experiments verify that the proposed method is better than the competing methods for non-redundant arrays at low signal-to-noise ratio.