Research on airport multitarget positioning and trajectory estimation by combining improved particle swarm and filtering algorithms

With the rapid development of intelligent algorithms and their wide penetration in the application fields of computational science and engineering, the problem of multi-target tracking in complex scenarios has increasingly become a research hotspot. This study focuses on the challenges of multi-target localization and motion trajectory estimation in the complex environment of airports, and innovatively proposes a hybrid intelligent computing framework. This framework achieves the collaborative optimization of positioning accuracy and computational efficiency by deeply integrating the improved quantum particle swarm optimization algorithm with the adaptive Kalman filter algorithm. The fitness function is defined by the error between multi-source measurement data and particle target position estimation, and the particle position estimation approximates the true target position to determine the multi-target position. The target trajectory is estimated by Kalman filtering algorithm, which is divided into two steps: estimation and update, and is realized by state transfer model and observation model. The target state estimation of the filtering algorithm is fed back to the improved particle swarm algorithm to correct its fitness function. By balancing the original fitness function and the fitness term based on filtering estimation, a more accurate airport multi-target trajectory estimation is achieved. The experimental results show that in the airport multi-target positioning and trajectory estimation, the comparison method has the problems of scattered offset of positioning points, error, confusion and omission in dense areas in positioning, and the maximum trajectory estimation errors are 15 meters, 30 meters, and 7 meters respectively; the proposed method has accurate positioning, and the trajectory estimation error is basically within 1 meter, which has significant advantages.