A track-before-detect method based on particle filters for target detection

Track-before-detect (TBD) is an approach which combines target detection and estimation on the basis of raw measurement. Compared with the conventional threshold methods, TBD can detect dim moving target with low signal-to-ratio (SNR). In this paper, a TBD algorithm based on Bayesian filtering framework is proposed to detect dim moving target from the infrared image sequence. The probability of target existence and the estimation of target state are treated separately. The posterior probability of target existence is presented as the closed-form solution. Particle filter is often designed to track target because of its flexibility. Thus this algorithm can be implemented using particle filters bank. The detector is achieved through interactive particle filters. The simulation experiment results show that the algorithm can detect and track dim moving target with low SNR. © 2013 Binary Information Press.