改进粒子滤波算法在深空红外小目标跟踪中的应用

Application of Improved Particle Filter Algorithm in Deep Space Infrared Small Target Tracking

非负矩阵分解具有较好的特征提取性能,广泛应用于数据融合领域,而粒子滤波则是一种处理非线性和非高斯动态系统状态估计的有效方法.该文结合两种算法的优点,提出了一种基于改进粒子滤波的红外小目标跟踪算法.利用NMF融合当前与之前的粒子分布权重,减小经典粒子滤波退化发散带来的精度误差.避免了目标遮挡及暂时消失带来的跟踪错误.仿真实验证明本文算法相对于经典粒子滤波,具有更好的跟踪精度和稳定性.

The non-negative matrix factorization （NMF） is widly used in data fusion for the advantage of feature extraction, and the particle filter （PF） is an effective method for the state estimation of non-linear and non-Gaussian dynamic systems. Therefore, an infrared small target tracking algorithm based on improved particle filter is proposed. Current and previous particle distribute weights are fused by NMF in order to reduce the precision error caused by particle divergence in classic PF method. So the tracking error of sheltered and disappeared target can be avoided. Experimental results show that the proposed method has better tracking precision and is more stability for small target tracking than the classic PF method.