Search

In the coherent signal environments caused by multipath propagation, the interference suppression performance of the global navigation satellite systems (GNSS) receivers decreases sharply. In this paper, a robust space-time adaptive processing (STAP) method for GNSS receivers is proposed to suppress interferences in coherent signal environments, by using the modified space-time two-dimensional iterative adaptive approach (ST2D-IAA) spectrum estimation. This method applies the IAA algorithm to the ST2D signal model of GNSS receivers, and further modifies the ST2D-IAA algorithm to accurately estimate the power spectrum and noise power simultaneously. The space-time interference-plus-noise covariance matrix (STINCM) is reconstructed by using the estimated power spectrum and noise power in the interference angle region. Based on the reconstructed STINCM, we construct the STAP beamforming optimization problem for the space-time steering vector (STSV) error vector, and further correct the STSV of GNSS signal. Finally, the weight vector of STAP beamforming is calculated by using the reconstructed STINCM and the corrected STSV of GNSS signal. Simulation results show that the proposed method can suppress interferences in coherent signal environments and outperforms the current methods. [ABSTRACT FROM AUTHOR]

The antispoofing method using the direction-of-arrival (DOA) feature can effectively improve the application security of the global navigation satellite system (GNSS) receivers. In this paper, a sparse reconstruction approach based on a coprime array of antennas is proposed to provide reliable DOA estimation under a GNSS spoofing attack. Specifically, the self-coherence property of genuine satellite signals and spoofing was fully exploited to construct a denoised covariance matrix that enables DOA estimation before receiver despreading. Based on this, an equivalent uniform linear array (ULA) was generated from the constructed covariance matrix via virtual array interpolation. By applying the ideal of sparse reconstruction to an equivalent ULA signal, the preliminary DOA estimation results could be obtained without the need for a number of signals. Considering that the sparse estimation technique suffers from basis mismatch effects, we designed an optimization problem with respect to off-grid error to compensate the initial DOA such that the performance loss of DOA estimation could be reduced. Numerical examples demonstrated the advantages of the proposed method in terms of degrees-of-freedom (DOFs), resolution and accuracy. [ABSTRACT FROM AUTHOR]