In this paper, we present a novel and efficient track-before-detect (TBD) algorithm based on multiple-model probability hypothesis density (MM-PHD) for tracking infrared maneuvering dim multi-target. Firstly, the standard sequential Monte Carlo probability hypothesis density (SMC-PHD) TBD-based algorithm is introduced and sequentially improved by the adaptive process noise and the importance re-sampling on particle likelihood, which result in the improvement in the algorithm robustness and convergence speed. Secondly, backward recursion of SMC-PHD is derived in order to ameliorate the tracking performance especially at the time of the multi-target arising. Finally, SMC-PHD is extended with multiple-model to track maneuvering dim multi-target. Extensive experiments have proved the efficiency of the presented algorithm in tracking infrared maneuvering dim multi-target, which produces better performance in track detection and tracking than other TBD-based algorithms including SMC-PHD, multiple-model particle filter (MM-PF), histogram probability multi-hypothesis tracking (H-PMHT) and Viterbi-like. First, the standard sequential Monte Carlo probability hypothesis (MM-PHD) for tracking infrared maneuvering dim multi-target. density (SMC-PHD) TBD-based algorithm is introduced and sequentially improved by the adaptive process noise and the importance re-sampling on particle likelihood, which results in the improvement in the algorithm robustness and convergence speed. PHD is derived in order to ameliorate the tracking performance especially at the time of the multi-target arising. Finally, SMC-PHD is extended with multiple-model to track maneuvering dim multi-target. Extensive experiments have proven the efficiency of the presented proposal in tracking infrared maneuvering dim multi-target, which produces better performance in track detection and tracking than other TBD-based algorithms including SMC-PHD, mul tiple-model particle filter (MM-PF), histogram probability multi-hypothesis tracking (H-PMHT) and Viterbi-like.