全球导航卫星系统广泛应用于军事、通信等多个领域,但导航信号容易受到空间中电磁干扰的影响,造成性能严重下降。文中基于信号分离的方法研究了欠采样情况下的干扰抑制问题,通过对干扰信号幅度调制信号的分解构建了稀疏观测模型,利用稀疏贝叶斯学习实现对干扰信号的重建,进一步从接收信号中剔除重建的干扰信号,得到干扰抑制后的导航信号。仿真结果表明:文中所提方法能够有效抑制导航信号中的干扰,提升导航信号的质量,在欠采样情况下该方法仍然具有较好的干扰抑制性能。

Global navigation satellite system is widely used in military, communications and other fields, but the navigation signal is vulnerable to electromagnetic interference in space, resulting in serious performance degradation. Based on the method of signal separation, the problem of interference mitigation in the case of missing samples is studied, a sparse observation model by decomposing the amplitude modulated signal of interference signal is constructed, sparse Bayesian learning is used to reconstruct the interference signal, the reconstructed interference signal from the received signal is further removed, and the navigation signal after interference mitigation is obtained. The simulation results show that the proposed method can effectively suppress the interference in the navigation signal and improve the quality of the navigation signal. The proposed method still has good interference mitigation performance in the case of missing samples.

TN974

国家自然科学基金资助项目