Dual-Grained Recursive Bayesian Filtering for UAV-Enabled Satellite User Terminal Surveillance

Low-Earth orbit (LEO) satellite communication is poised to be a key enabler in the burgeoning 6G era. However, the emergence of unauthorized LEO satellite user terminals (LSUTs) poses significant threats to communication security, creating an urgent need for effective LSUT surveillance. Nevertheless, LSUT surveillance is challenging since the transmit beamforming of LSUT leads to the highly directional uplink beam, which complicates the reception of high signal-to-noise ratio (SNR) signals necessary for accurate geolocation and monitoring by surveillance equipment. To handle the above issues, we develop a dual-grained recursive Bayesian filtering scheme for unmanned aerial vehicle (UAV)-enabled spectrum surveillance. Specifically, we construct an LSUT uplink signal detection probability map, enabling a coarse-grained search to identify the surveillance region of interest (SROI). This procedure enhances the SNRs of received signals and improves the initialization of LSUT monitoring. Furthermore, we propose a particle filter with beam motion mode tracker for fine-grained monitoring, which facilitates accurate geolocation and robust monitoring despite dynamic beam pointing direction and satellite handovers. Numerical results demonstrate that our dual-grained trajectory planning approach significantly benefits LSUT surveillance, yielding superior cumulative detection probability, monitoring accuracy, SNRs of received signals, and geolocation accuracy.