We present a particle filter (PF)-based algorithm to detect and track maneuvering infrared weak multiple targets at different signal-to-noise ratios for the scenes with the multiple targets number unknown and varying. A detecting filter and a tracking filter based on sequential likelihood ratio (LR) testing with fixed sample size are designed, respectively, for capturing new target and tracking confirmed targets. The algorithm is optimized with selectively particles sampling and adaptive process noise. Targets birth and death time are accurately estimated according to the change degree of the LR along with the corresponding state amended through PF backward recursion. Simulation results show that it is positive to detect and track maneuvering infrared weak multiple targets with the appearance and disappearance of more than one, which also achieves a significant improvement in state estimation especially for the time targets which appear and disappear.