针对低信噪比(SNR<3 dB)场景下弱小目标跟踪问题，提出了改进的粒子滤波跟踪方法。本文首先通过空间位置加权的方式来获取灰度特征，并将邻域运动模型和灰度概率图相结合来获取弱小目标运动特征，然后构建灰度与运动特性的联合观测模型来计算粒子权值。同时在跟踪过程中考虑到目标的灰度分布特性并不稳定，加入了自适应更新参考目标灰度模板的策略，最后采用几组真实场景来验证本文算法的跟踪效果。实验证明：和传统算法相比，本文算法增强了低信噪比(SNR<3 dB)场景下红外弱小目标跟踪能力。

As to solve the problem of dim small target tracking in low signal-to-noise ratio (SNR<3 dB) scenes, an improved particle filter tracking method is proposed. This paper firstly obtains the gray feature by spatial position weighting method, and combines the neighborhood motion model and the gray probability graph to get the motion features of dim small target. Then construct the joint observation model of gray and motion features to calculate the particle weights. At the same time, in the process of tracking, the gray distribution of the target is not stable, and the strategy of adaptively updating the gray template of reference target is added. Finally, the sequence image is used to prove the tracking effect of dim small target. Experiments show that compared with the traditional particle filter algorithm, the proposed method greatly enhanced the tracking ability of dim small target in low SNR (SNR<3 dB) scenes.