Efficient Bayesian Track-Before-Detect

This paper presents a novel Bayesian recursive track-before-detect (TBD) algorithm for detection and tracking of dim targets in optical image sequences. The algorithm eliminates the need for storing past observations by recursively incorporating new data acquired through sensor to the existing information. It calculates the likelihood ratio for optimal detection and estimates target state simultaneously. The technique does not require velocity-matched filtering and hence, it is capable of detecting any target moving in any direction. The algorithm is tested with both synthetic and real video sequences, and is shown to be capable of performing sufficiently well for very low signal-to-noise ratio situations.