运动目标与静止目标混合的DOA估计

C. Wan, Yubing Han, Weixing Sheng, Xiaofeng Ma, Ren-li Zhang
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摘要

在对到达方向(DOA)估计的许多情况下,作为目标信号的运动目标与静止目标同时存在。为了在消除静止信号的同时获得运动目标的doa,提出了一种基于多信号分类的均匀线性阵列(ULA)方法。处理后的协方差矩阵是一个仅包含运动目标信息的非正定矩阵,通过奇异值分解(SVD)给出信号子空间和噪声子空间。众所周知,在基于子空间的方法中需要进行源数估计。然而,即使在高信噪比的情况下,基于信息论准则的传统方法由于奇异值的特殊分布而不能很好地工作。因此,根据处理后的协方差矩阵奇异值的性质,给出了一种相对鲁棒的估计移动源数目的方法。仿真实例验证了该方法的性能和有效性。
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The DOA estimation of moving targets mixed with stationary targets
In many cases for direction of arrival (DOA) estimation, moving targets which are the signals of interest, exist with stationary targets simultaneously. To obtain the DOAs of moving targets while eliminating the stationary signals, a new method based on multiple signal classification (MUSIC) for uniform linear array (ULA) is presented in this paper. The signal and noise subspaces are given by singular value decomposition (SVD) of the processed covariance matrix which is a non-positive definite matrix and includes the information only about moving targets. It is well known that source number estimation is needed in subspace-based methods. However, the traditional methods based on information theoretic criteria couldn't work well due to the special distribution of the singular values even with high signal-to-noise ratio (SNR). Thus according to the property of singular values of the processed covariance matrix, we give a relative robust approach to estimate the number of moving sources. Several simulation examples are provided to show the performance and effectiveness of the proposed method.
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