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-li...

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.