Modern airborne radar systems have been equipped with multi-dimensional and full-airspace capabilities for detection and tracking. With the increasing maneuverability of airborne targets and the complexity of the electromagnetic environment, target tracking, as a key radar technology, is facing ever-growing demands for tracking accuracy and real-time response capability. In response to these challenges, filtering algorithms for air-to-air multi-target tracking in airborne radar systems are investigated in this thesis. To address the low tracking accuracy of traditional Interactive Multiple Model (IMM) algorithms for maneuvering targets, an Adaptive IMM Particle Filter is proposed by correcting the Markov transition probability matrix. Model matching and switching responsiveness are enhanced by the proposed method. Simulation results demonstrate that position tracking accuracy is improved by 16.1%, and velocity tracking accuracy is improved by 26.8%, compared to conventional approaches, highlighting its significant advantage in maneuverability adaptation.