Ji Yunhao, Lu Yaobing, Wei Shan, Li Zigeng
Beijing Institute of Radio Measurement, Beijing 100854, China.
Sensors (Basel). 2022 Nov 4;22(21):8494. doi: 10.3390/s22218494.
When the desired signal and multiple mainlobe interferences coexist in the received data, the performance of the current mainlobe interference suppression algorithms is severely challenged. This paper proposes a multiple mainlobe interference suppression method based on eigen-subspace and eigen-oblique projection to solve this problem. First, use the spatial spectrum algorithm to calculate interference power and direction. Next, reconstruct the eigen-subspace to accurately calculate the interference eigenvector, then generate the eigen-oblique projection matrix to suppress mainlobe interference and output the desired signal without distortion. Finally, the adaptive weight vector is calculated to suppress sidelobe interference. Through the above steps, the proposed method solves the problem that the mainlobe interference eigenvector is difficult to select, caused by the desired signal and the mismatch of the mainlobe interference steering vector and its eigenvector. The simulation result proves that our method could suppress interference more successfully than the former methods.
当期望信号与多个主瓣干扰在接收数据中共存时,当前主瓣干扰抑制算法的性能受到严峻挑战。针对这一问题,本文提出一种基于特征子空间和特征斜投影的多主瓣干扰抑制方法。首先,利用空间谱算法计算干扰功率和方向。其次,重构特征子空间以精确计算干扰特征向量,进而生成特征斜投影矩阵来抑制主瓣干扰并无失真地输出期望信号。最后,计算自适应权向量以抑制旁瓣干扰。通过上述步骤,所提方法解决了由于期望信号与主瓣干扰导向向量及其特征向量失配导致的主瓣干扰特征向量难以选取的问题。仿真结果表明,与以往方法相比,本文方法能更成功地抑制干扰。