Carbon materials with quasi-graphene layers: the dielectric, percolation properties and the electronic transport mechanism

We investigate the dielectric properties of multi-walled carbon nanotubes (MWCNTs) and graphite filling in SiO₂ with the filling concentration of 2-20 wt.% in the frequency range of 10²-10⁷ Hz. MWCNTs and graphite have general electrical properties and percolation phenomena owing to their quasi-structure made up of graphene layers. Both permittivity ε and conductivity σ exhibit jumps around the percolation threshold. Variations of dielectric properties of the composites are in agreement with the percolation theory. All the percolation phenomena are determined by hopping and migrating electrons, which are attributed to the special electronic transport mechanism of the fillers in the composites. However, the twin-percolation phenomenon exists when the concentration of MWCNTs is between 5-10 wt.% and 15-20 wt.% in the MWCNTs/SiO₂ composites, while in the graphite/SiO₂ composites, there is only one percolation phenomenon in the graphite concentration of 10-15 wt.%. The unique twin-percolation phenomenon of MWCNTs/SiO₂ is described and attributed to the electronic transfer mechanism, especially the network effect of MWCNTs in the composites. The formation of network plays an essential role in determining the second percolation threshold of MWCNTs/SiO₂.     

基于稀疏重构的空域大目标背景下的小目标检测方法

现代战争经常需要大小平台协同作战,较为典型的是有/无人编队协同作战。在协同作战过程中,由于大目标的回波副瓣掩盖了小目标回波,导致无法实现大小目标一体化检测,给指挥员战场态势的判断带来了严峻的挑战。为此研究了一种基于稀疏重构的空域大目标背景下的小目标检测方法,利用压缩感知稀疏重构算法的高分辨特性实现大小目标在空域上分离,进而消除大目标对小目标的检测影响。针对传统稀疏重构方法存在重构准确率与计算效率相互矛盾的问题,提出了一种基于虚变换的稀疏重构方法。仿真分析结果表明,所提方法相对于传统正交投影(Orthogonal Matching Projection, OMP)方法具有更好的大小目标分离准确度,而相对于传统凸优化重构方法,可以实现计算效率提升16%。     

一种基于失配滤波的5G模糊函数副峰抑制技术

5G信号具有大带宽、高载频、发射资源丰富等优点，在城市或郊区等区域低空目标探测领域具有广阔的应用前景。但5G信号中包含的同步信号、控制信号等确定性信号导致其模糊函数并不是理想的图钉状，而是存在较多的模糊副峰，严重制约了5G辐射源雷达的探测性能。针对上述问题，首先深入分析了5G信号模糊函数特性及副峰产生机理，然后在副峰认知的基础上提出一种基于失配滤波的5G模糊函数副峰抑制方法。理论分析和仿真结果表明，所提方法可实现对距离与频域维副峰一体化抑制，从而能有效减少5G辐射源雷达目标探测过程中由副峰产生的虚警和漏警。