Robust blind space‐time adaptive processing for measurement error mitigation in GNSS receivers

Abstract

AbstractThe measurement errors induced by the space‐time adaptive processing (STAP) is gaining attention for its significant detriment to the precision of the global navigation satellite system (GNSS) receiver positioning. To mitigate measurement errors, the steering vector (SV) estimation method based on spreading is widely employed in measurement error mitigation algorithms. However, the hazard of the SV estimation fluctuation problem is ignored in these algorithms. In this paper, the specific harm of such SV estimation fluctuation problem is analysed. To alleviate such problem and to eliminate the measurement errors as much as possible, a robust STAP beamformer for GNSS receivers is proposed. First, to acquire a series of robust SVs in different integration times, a desired signal covariance (DSC) matrix is iteratively reconstructed to remove the disturbance from thermal noise and the residual jamming signals. Second, to eliminate measurement errors, a replacement matrix is formed to help guarantee the phase linearities of the tapped delay lines (TDLs). Numerical examples demonstrate that the method can achieve a set of stable SV estimations and linear phase TDLs, leading to a carrier‐to‐noise‐power ratio () of more than 50 dBHz and a code phase bias of less than 3.4 m, which outperform the methods used for comparison.