AI based Pointing and Tracking for Satcom Terminals

Aimed at maximizing the antenna signal of the satellite communication (Satcom) terminal in a satellite system, positioning and tracking methods based on AI are studied in this project. This proposal focuses on the issue of a satellite’s pointing and tracking for a stationary Satcom terminal. The detailed methods includes the following four functional components: 1) data filtering and preprocessing of original sensory data collected by Satcom terminal; 2) data error corrections based on multimodal data fusion; 3) direction search to obtain maximum signal strength based on historical data-based learning, and self-learning; and 4) reinforcement learning based optimization of antenna vector to obtain maximum signal strength. The goal is to realize the high accuracy of satellite pointing and optimize the relocation of the Satcom terminal’s antenna for satellite tracking. The target of the proposed AI-enabled self-aligned algorithm is to enable Satcom terminal to achieve industry leading low acquisition time.

Jeungeun Song
Faculty Supervisor: 
Victor Leung
British Columbia
Partner University: