Dual-polarized sensitive arrays (DPSAs) with the space–time-polarization adaptive processing (STPAP) technique, which employs the polarization domain as well as the space domain and time domain to filter out interferences, can cancel a larger number of wideband interferences for GNSS receivers. However, the traditional STPAP beamforming algorithm, which requires a separate adaptive filter for each GNSS satellite, will make the process computationally intensive as there are multiple GNSS satellites in the field of view (FOV). In order to overcome the shortcoming, a novel STPAP beamforming algorithm based on the minimum variance distortionless response (MVDR) criterion is proposed. Compared with the traditional STPAP beamforming algorithm, the proposed STPAP beamforming algorithm can process multiple GNSS satellites at once using only one adaptive filter, which will greatly reduce the computational complexity. Moreover, the proposed algorithm will not lead to a sharp deterioration in the output carrier-to-noise density ratio (C/N₀) performance if the number of GNSS satellites processed in the same adaptive filter is proper. Furthermore, to calculate weight vector iteratively, an adaptive algorithm based on the constrained least mean square (CLMS) method is derived for the proposed STPAP beamforming algorithm. Simulation results validate that the proposed algorithm is effective in mitigating interferences for GNSS receivers in the joint space–time-polarization domain and meanwhile has lower computational complexity when maintaining the output C/N₀ performance close to that of the traditional STPAP algorithm.

Navigation spoofing has been widely utilized in unmanned aircraft vehicle (UAV) countermeasures, due to its advantages of covertness, effectiveness, and dynamic trajectory control ability. However, existing research faces two primary challenges. Firstly, sudden changes in the target UAV’s trajectory can result in a significant degradation in the spoofing performance, which may enable the onboard inertial components to detect and identify the ongoing spoofing attempts. Secondly, gradual accumulation of control errors over time degenerates the spoofing effect. To address these problems, we propose a dynamic trajectory spoofing approach for UAVs based on model predictive control (MPC), which progressively steers the UAVs towards the predetermined trajectory of the spoofer. Simulation results demonstrate a substantial enhancement in dynamic trajectory control performance and decrease in accumulation error compared to the existing methods.