Target tracking is the basic function of airborne ADS-B surveillance applications. Improving the target tracking performance of weak maneuvering airliner around the aircraft is of great significance for mastering the traffic situation and improving flight safety. Therefore, a target tracking method for ADS-B surveillance application based on interactive multiple model Kalman filter (IMMKF) algorithm is proposed. Firstly, aiming at the flight characteristics of airliner under the background of weak maneuver, a set of motion models including constant velocity model and standard coordinated turning model is established, and the models are linearized and approximated; Then, the model prediction and ADS-B state vector measurement data are used as the input of multiple parallel Kalman filters in IMMKF algorithm for parallel filtering; Finally, the estimation of the target state vector and the model approximation probability are calculated and used as input for the next iteration. The simulation results show that compared with the Kalman filter target tracking method based on the constant velocity model, the position tracking error of IMMKF method is reduced by 59%, and the velocity tracking error is reduced by 77%, which significantly improves the state estimation performance, and has high tracking accuracy, robustness and computational efficiency. It has practical application value and reference significance in ADS-B surveillance applications.