A novel track-before-detect algorithm based on optimal nonlinear filtering for detecting and tracking infrared dim target

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.