论文标题
使用移动阵列的离网多源被动本地化
Off-grid Multi-Source Passive Localization Using a Moving Array
论文作者
论文摘要
本文提出了一种新型的直接被动定位技术，该技术是在本文中使用空间域中阵列协方差矩阵的稀疏表示。测量是通过在不同观察位置堆叠所有阵列协方差矩阵的矢量化版本来构建的。首先，开发了基于网格的压缩传感（CS）方法，其中字典由从搜索网格到观测位置的转向向量组成。凸优化用于解决`1-norm最小化问题。其次，为了获得更优质的目标位置，我们开发了一种基于网格的CS方法，其中大化最小化技术将每次迭代中的ATAN-SUM目标函数替换为二次凸功能，可以轻松最小化。目标函数ATAN-SUM与“ 0- norm”更相似，并且比原木函数更具稀疏性。在低SNR条件下，该方法的工作原理更强，而在传统的情况下，所需的观察位置比传统的观察位置更少。模拟实验验证了所提出的算法的承诺。
A novel direct passive localization technique through a single moving array is proposed in this paper using the sparse representation of the array covariance matrix in spatial domain. The measurement is constructed by stacking the vectorized version of all the array covariance matrices at different observing positions. First, an on-grid compressive sensing (CS) based method is developed, where the dictionary is composed of the steering vectors from the searching grids to the observing positions. Convex optimization is applied to solve the `1-norm minimization problem. Second, to get much finer target positions, we develop an on-grid CS based method, where the majorization-minimization technique replaces the atan-sum objective function in each iteration by a quadratic convex function which can be easily minimized. The objective function,atan-sum, is more similar to `0-norm, and more sparsity encouraging than the log-sum function.This method also works more robustly at conditions of low SNR, and fewer observing positions are needed than in the traditional ones. The simulation experiments verify the promises of the proposed algorithm.
