多基站非圆信号直接定位：降维PM与泰勒补偿

针对现有多阵列非圆(NC)信号直接定位方法(DPD)谱峰搜索计算复杂度高，对基站的位置比较敏感，没有考虑信号在空间中传播时的损耗差异，导致估计性能不稳定的问题，提出一种联合降维传播算子与泰勒补偿(JRT-PM)的非圆信号直接定位算法。首先根据非圆信号的椭圆协方差信息扩展阵列孔径，通过降维方法消除非圆相位搜索维度进行粗估计降低计算复杂度，然后联合所有基站的信息进行泰勒补偿提升算法估计性能。仿真实验表明，相比于传统到达角K均值聚类(AOA-clustering)两步定位算法、最小均方无畸变响应(MVDR)直接定位算法、子空间数据融合(SDF)直接定位算法，所提算法在提升定位精确度的同时可以估计更多目标；与非圆传播算子(NC-PM)直接定位相比，所提算法在保证估计性能的同时显著降低了计算复杂度。

DPD of NC signals with multiple base stations: Reduced Dimension Propagator Method and Taylor compensation

There exist the following problems in the available methods for Direct Position Determination(DPD) of Non-Circular(NC) sources with multiple base stations: high computational complexity of spectral peak search, sensitivity to the location of base stations, and lack of consideration of the difference in signal loss when propagating in space, which leads to unstable performance. A DPD method is proposed for NC sources: Joint Reduced Dimension Propagator Method and Taylor Compensation(JRT-PM). First, the array aperture is expanded according to the elliptic covariance information of NC signals. Then, the NC phase search dimension is eliminated by the dimension reduction method for rough estimation to reduce the computational complexity. Next, the Taylor compensation is combined with the information of all base stations to improve the estimation performance of the algorithm. Simulation experiments show that compared with the traditional two-step localization algorithm by Angle Of Arrival K-means clustering(AOA-clustering), Minimum Variance Distortionless Response(MVDR) DPD method and Subspace Data Fusion(SDF) DPD method, the proposed algorithm can estimate more targets while improving the localization accuracy. Compared with Non-Circular Propagator Method(NC-PM) DPD method, the proposed algorithm significantly reduces the computational complexity while ensuring the estimation performance.