未知互耦影响下的多阵直接定位:基于子空间数据融合与降维搜索

Direct Position Determination with Multi-array with Unknown Mutual Coupling:Based on Subspace Data Fusion and Reduced-Dimension Search

为了解决子空间数据融合(Subspace data fusion,SDF)算法用于未知互耦影响下的分布式多阵列定位时定位精度低的问题,本文结合降维搜索思想提出了一种降互耦维度的子空间数据融合(Reduced mutual coupling dimension subspace data fusion,RMCD⁃SDF)方法。该方法首先将互耦误差模型引入SDF算法,使其适应于天线阵列受到未知互耦误差影响的场景。在此基础上,为了降低同时搜索所有未知参数带来的超高计算复杂度,本文引入降维搜索思想并构造了RMCD⁃SDF算法谱函数。仿真结果显示,RMCD⁃SDF算法的定位性能在阵列受到未知互耦影响的场景下具有优势,与现有算法相比计算复杂度接近,但是具有更高的定位精度。在10 dB信噪比下本文算法的定位均方根误差相比经典的SDF算法降低了8.67 dB。

To improve the localization accuracy of the subspace data fusion(SDF)applied in distributed multi-array under the influence of unknown mutual coupling,we propose a reduced mutual coupling dimension subspace data fusion(RMCD-SDF)approach in this paper.Firstly,we introduce the mutual coupling error model into the SDF approach to make it adapt to the scenario where the antenna array is affected by the unknown mutual coupling error.Furthermore,in order to reduce the ultra-high computational complexity caused by searching all unknown parameters simultaneously,we introduce the reduced-dimension idea and construct the spectral function of RMCD-SDF.Simulation results show that the RMCD-SDF approach has advantageous localization performance when arrays are affected by unknown mutual coupling.The RMCD-SDF approach has similar computational complexity but higher localization accuracy than existing algorithms.When the signal-to-noise ratio(SNR)is 10 dB,the root means square error of the proposed approach is 8.67 dB lower than the classical SDF algorithm.