针对目标在真实红外场景图像中的非线性、非高斯特性,从非线性理论出发构建状态和观测模型,提出了一种基于最优非线性滤波算法的红外弱小目标检测前跟踪算法。采用基于谱分离思想的Wiener混沌展开方法对构建的随机微分方程进行求解,并将算法结构分为两部分：一部分与观测数据无关且耗时的计算放在观测前期完成;另一部分是与观测数据有关的实时快速计算放在后续执行,以提高运算效率。仿真结果表明,该算法在低信噪比红外目标检测中表现出了良好的检测性能,更适合实时应用。

Aiming at the nonlinear and non-Gaussian features of the real infrared scenes,an optimal nonlinear filtering based on algorithm is proposed for the infrared dim target tracking-before-detecting application. It uses the nonlinear theory to construct the state and observation models and uses the spectral separation scheme based on Wiener chaos expansion method to resolve the stochastic differential equation of the constructed models. In order to improve computation efficiency,the most time-consuming operations independent of observation data are processed on the fore observation stage. The other observation data related rapid computations are implemented subsequently. Simulation results show that this algorithm possesses excellent detection performance and is more suitable for real-time processing.