基于矩阵差分的远场和近场混合源定位方法

混合源定位在无源雷达中发挥着重要作用。针对均匀圆阵下基于相位差方法的定位精度较低的问题，该文提出基于矩阵差分的远场和近场混合源定位方法。首先，利用二维多重信号(2-D MUSIC)分类方法估计出远场源的方位角和俯仰角；随后，利用协方差矩阵差分方法提取出近场源差分矩阵，通过改进的类旋转不变估计信号参数(ESPRIT-like)方法计算出近场源的方位角和俯仰角；进一步地，利用一维多重信号分类方法估计出近场源的距离；最后通过仿真实验对该文所提算法进行验证。该文所提算法在远场源和近场源角度相同的情况下能够有效地识别混合源，并且提高混合源参数估计精度。实验结果表明该算法在信噪比(SNR)为20 dB时，近场源的二维DOA估计误差接近0.01°，而近场源的距离误差接近0.1 m。 

Matrix Differencing Method for Mixed Far-field andNear-field Source Localization

Mixed source localization plays an important role in passive radars. Aiming at the problem of low accuracy via phase difference method under a uniform circular array, this paper proposes a matrix differencing method for mixed far-field and near-field source localization. First, a two-dimensional MUltiple SIgnal Classification (MUSIC) method was utilized to estimate the azimuth and elevation angles of far-field sources. Thereafter, the covariance matrix difference method was exploited to extract the difference matrix of near-field sources. The azimuth and elevation angles of the far-field sources were estimated using the Estimation of Signal Parameters via Rotational Invariance Techniques-like (ESPRIT-like) method. Furthermore, the distance of the near-field sources was obtained by the one-dimensional MUSIC method. Finally, simulations were performed to verify the performance of the proposed algorithm. The proposed algorithm could effectively identify the mixed source when the two-dimensional Direction-Of-Arrival (DOA) of the far-field and near-field sources were the same. Moreover, the proposed algorithm could improve the accuracy of the mixed source parameter estimation. Results show that when the signal-to-noise ratio was set to 20 dB, the 2-D DOA estimation error of the near-field source was approximately 0.01°, and the distance error of the near-field source was approximately 0.1 m.