Mitigation of Non-stationary Jammings with Missing Samples for GNSS Using Sparse Representation

Abstract

Global navigation satellite systems (GNSS) are widely used in most civil and military applications. However, GNSS receivers suffer from severe performance degradation when jammed by electromagnetic interference. This paper considers the mitigation problem of non-stationary jammings with missing samples for GNSS. We propose to reconstruct the interference by sparse representation utilizing the sparsity of jammings in the time-frequency (TF) domain. The GNSS signal is recovered by eliminating the reconstructed interference from the received signal. First, the instantaneous frequencies (IFs) of jammings are extracted from adaptive directional TF distributions. Then, we construct the over-complete atomic dictionary based on the estimated IFs. The interference is reconstructed by orthogonal matching pursuit (OMP) algorithm. The proposed method can achieve the mitigation of non-stationary jamming and the acquisition of GNSS signals in the presence of missing samples. We confirm the effectiveness of the algorithm by simulations.