Accurate trajectory prediction plays a fundamental role in advanced air traffic control operations, because it forms the basis for (among others) conflict detection and resolution schemes. It has been demonstrated that improved trajectory prediction accuracy can be achieved using radar measurements for a single aircraft, but the benefits are expected to be much greater if one can fuse measurements from multiple aircraft at different locations and time instants. It is shown here how this multi-aircraft sensor fusion problem can be formulated as a high-dimensional state estimation problem. A novel particle filtering algorithm is developed to solve it in realistic scale situations. By exploiting the structure of the problem, one can address the technical challenges that arise in the process: efficiently handling the information, dealing with the estimation of a very high-dimensional state, and dealing with the nonlinear dynamics of aircraft motion and control. The effectiveness of the novel algorithms is demonstrated on feasibility studies involving multiple aircraft (from one to several hundred). The studies show that in the presence of multiple aircraft the trajectory prediction results approach the theoretical limit ofaccuracy under these conditions.