School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China.
Sensors (Basel). 2022 Jan 28;22(3):1042. doi: 10.3390/s22031042.
Due to the introduction of frequency offsets, the pattern synthesis problem of sparse Frequency diverse array (FDA) becomes more complicated than that of the phased array. A typical way to solve this problem is to use a global optimization algorithm, but this is usually time-consuming. In this paper, we propose an efficient non-iterative beampattern synthesis approach for sparse FDA. For a given reference pattern, which can be generated by other synthesis methods, we first sample it uniformly and construct the Hankel matrix with the sampled data. By low-rank processing, a low-rank approximation version of the Hankel matrix can then be obtained. Finally, the matrix enhancement and matrix pencil (MEMP) and matrix pencil (MP) methods are applied to estimate the antenna positions, frequency offsets, and excitations of the obtained array from the approximated matrix. Besides this, two typical FDA frameworks including multi-carrier FDA (MCFDA) and standard FDA (SFDA) are considered. Numerical simulation results prove that the proposed method outperforms the existing methods in terms of synthesis error, average runtime, and percentage of saving elements.
由于频率偏移的引入,稀疏频分阵列(FDA)的模式综合问题变得比相控阵更加复杂。解决这个问题的一种典型方法是使用全局优化算法,但这通常很耗时。在本文中,我们提出了一种用于稀疏 FDA 的高效非迭代波束形成方法。对于给定的参考模式,可以通过其他合成方法生成,我们首先对其进行均匀采样,并使用采样数据构造汉克尔矩阵。通过低秩处理,可以得到汉克尔矩阵的低秩逼近版本。最后,应用矩阵增强和矩阵束(MEMP)和矩阵束(MP)方法从近似矩阵中估计得到的阵列的天线位置、频率偏移和激励。此外,还考虑了两种典型的 FDA 框架,包括多载波 FDA(MCFDA)和标准 FDA(SFDA)。数值模拟结果证明,与现有方法相比,该方法在综合误差、平均运行时间和节省元素的百分比方面表现更好。