Marginalized Particle Filter based Track-Before-Detect Algorithm for Small Dim Infrared Target

Small dim infrared target detection and tracking is the key technology of the infrared surveillance system. The track-before-detect(TBD) algorithm can integrate the unthresholded measurements over time to track the low signal-to-noise ratio target. In this paper, a marginalized particle filter based TBD algorithm is proposed for small dim infrared target detection and tracking. By marginalizing out the states appearing linearly in the small dim infrared target dynamic model, the marginalized particle filter can estimate the nonlinear states using the particle filter and estimate the linear states using the Kalman filter. It is confirmed that the high–dimensional model can be based on a particle filter using marginalization for all but three states. Simulation results show that the proposed algorithm is capable of detecting and tracking small dim targets efficiently.