In contemporary aviation, automated landing technology for aircraft is becoming increasingly vital. Achieving high precision and reliable automated landing, however, requires addressing several technological challenges. One of these challenges is the issue of multi-sensor data fusion. During the aircraft landing process, GPS, radar, and inertial navigation system (INS) are three crucial sensors, providing positional, velocity, and attitude information respectively. Each of these sensors has its own advantages and disadvantages; when used independently, they are susceptible to external environmental interferences and intrinsic errors, resulting in insufficient overall navigation precision. This paper proposes a federated filtering method based on Extended Kalman Filter (EKF) to improve navigation accuracy and enhance system stability through the fusion of multiple sensor data. By thoroughly analyzing multi-sensor errors and conducting simulation experiments, this paper will demonstrate the application effects and advantages of this method in the aircraft landing process, and discuss its theoretical and technical innovations.