A Satellite Selection Strategy of SURF IA in Airport Intelligent Monitoring

Abstract

The implementation of Enhanced Traffic Situational Awareness on the Airport Surface with Indications and Alerts (SURF IA) monitoring in airports improves the safety and efficiency of airport surface operation. Navigation accuracy category for position (NACP), navigation accuracy category for velocity (NACV), and other data are obtained from Automatic Dependent Surveillance-Broadcast (ADS-B) IN, and these data will be the only data source of SURF IA. In this paper, we use the Beidou System (DBS) and Global Positioning Symbol (GPS) dual system as the positioning data source of the ADS-B. In cases where all visible satellites meet the performance requirements of the SURF IA, particle swarm optimization (PSO) and a new particle swarm optimization (APSO) are used to screen the integrated navigation satellites to meet the requirements of SURF IA by using fewer satellites, and the satellite selection ability of the two is compared. The simulation results show that under the same conditions, by using fewer navigation satellites can meet the minimum monitoring performance requirements for the implementation of SURF IA operation. And the star selection ability of APSO is better. This improves the performance of airport surface intelligent monitoring.