A robust STAP beamforming algorithm for GNSS receivers in high dynamic environment

Abstract In high dynamic environment, the output performance of the space-time adaptive processing (STAP) beamformer can be degraded dramatically since directions of arrival (DOAs) corresponding to interferences change rapidly. A novel robust STAP beamforming algorithm is proposed for global navigation satellite system (GNSS) receivers to broaden the nulls towards interferences and meanwhile fight against the steering vector (SV) mismatch caused by DOA estimation errors. Firstly, the received signal model in high dynamic environment is established for the STAP architecture. Then, the spatial-temporal interference plus noise covariance (INC) matrix is obtained by reconstructing the interference covariance matrix and noise covariance matrix respectively to avoid DOA estimation of GNSS signals, which can also remove the signal of interest (SOI) component from the sample covariance matrix (SCM). Furthermore, the spatial-temporal spectrum estimates around interferences are respectively reset to be the same as those of interferences, due to which the nulls towards interferences in the space domain can be widened in high dynamic environment. Finally, the constraints to solve weight vector is specially designed for GNSS signals. Simulation results show that the proposed algorithm can broaden the nulls effectively and also has a better output carrier-to-noise (C/No) ratio performance than the other involving algorithms.