基于运动基阵的离网多源无源定位
Off-grid Multi-Source Passive Localization Using a Moving Array
摘要：
利用阵列协方差矩阵在空间域的稀疏表示，提出了一种新的单移动基阵直接无源定位方法。通过堆叠不同观测位置处的所有阵列协方差矩阵的矢量化版本来构造测量。首先，基于网格上压缩感知（CS）的方法被开发，其中字典由从搜索网格到观察位置的导向矢量组成。凸优化被应用于解决`1-范数最小化问题。其次，为了获得更精细的目标位置，我们开发了一种基于网格CS的方法，其中优化最小化技术在每次迭代中用一个可以很容易地最小化的二次凸函数代替atan和目标函数。该方法的目标函数atan-sum更接近于“0-范数”，比对数和函数更具有稀疏性，在低信噪比条件下也具有更强的鲁棒性，并且所需的观测位置比传统方法少。仿真实验验证了该算法的有效性。
英文摘要：
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.
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