A Novel Track-before-Detect Algorithm for Small Dim Infrared Target

A novel track-before-detect filtering algorithm is proposed for small dim infrared targets with low signal-to-noise ratio under complex backgrounds. A new particle filter called Quasi-Monte Carlo sampling based Gaussian particle filter(QMC-GPF) is developed to estimate on-line the standard kinematic parameters of the target, including position and velocity, as well as the amplitude of the target. The convergence characteristic of the covariance matrix of the posterior densities propagated in the QMC-GPF is used to determine whether it is the true target. The algorithm is tested with a synthetic target in IR image sequences, and it is proved that the algorithm is capable of performing sufficiently well for dim target of  SNR¨R1dB.