基于仿射群的自调整粒子滤波算法被提出，用于跟踪机场内泊位飞机，防止飞机发生相碰。算法中主要是利用最少粒子数量找到最优状态跟踪目标，在线学习估计器来交替地调整这些粒子，根据反馈出现相似分数使得这些粒子朝着邻近的最优状态移动，当所有调整的粒子满足目标块最大相似度或者允许的最大粒子数量达到时结束。算法实现了稀少采样，能够获得更好的鲁棒性和高准确率跟踪的效果。
The algorithm based on affine group self-tuning particle filtering is proposed to track docking air-craft in airport, and to prevent the aircraft collision. The algorithm is mainly used to find the op-timal state with minimum number of particles tracking target. The online learning estimator is used to adjust these particles, the similarity score is based on the feedback, which makes the par-ticles move towards the optimal state. It comes to an end when all the particles are adjusted to meet the maximum degree of similarity or the maximum number of particles allowed. The algorithm achieves sparse sampling and obtains better robustness and high accuracy tracking results.