镜向多路径模型在低角跟踪中的应用

Application of Specular Multipath Model in Low-Angle Tracking

在存在多路径反射的低角跟踪中,当信源相关时,传统的多信号分类(MUSIC)算法和其它一些超分辨技术一样,空间谱估计性能会明显变差。为了克服这一缺点,用一组模拟镜向多路径的特殊向量代替波达矢量(DOA)。把镜像多路径模型应用在最大似然(ML)估计中,其性能得到了很大的改善。最后,应用同一镜像多路径模型,对MUSIC算法和ML算法的性能进行比较,给出仿真结果,结果表明,应用同一镜向多路径模型,且其它条件相同的情况下,在高信噪比时,ML算法和MU-SIC算法性能相当,但在低信噪比时,由于ML算法需要的快拍数少而更有效,且计算量小。

When signals are highly correlated, the performance of Multiple Signal Classification algorithm as well as several other superresolution methods for spatial spectrum estimation degrades severely in low - angle tracking involving multipath reflection. The defect can be overcome by replacing the DOA vector with a special vector modeling the specular multipath. When the special vector is used in ML estimation, its performance is improved perfectly. At last, the paper uses the same specular multipath reflection model in ML arithmetic and MUSIC arithmetic, then compares their performances and achieves the results. The results indicate that both algorithms perform similarly for high signal- to- noise ratios but the ML approach is preferred because it is more efficient, requires less snapshots and gives better results for low signal - to - noise ratios.