Optimized Multiple Model Particle Filter Track-Before-Detect Algorithm for Maneuvering Weak Target

The multiple model particle filter track-before-detect (MMPF-TBD) is currently one of the mainstream research methods in tracking and detection area for maneuvering weak target, which utilizes as many dynamic models as possible to match the motion of the target. This method accords with the research direction of models’ division; however, the complex transformation and low efficiency of the models always trouble us. Considering the similarity of the models in structure, we proposed an optimized multiple model particle filter track-before-detect algorithm in this paper, which generates many models from one framework by the change of particles’ acceleration. It reduces the complexity of MMPF-TBD and improves the precision of state estimation as we use auxiliary particle filter to implement it. Finally the simulation shows it has a more stable performance in detecting and tracking a maneuvering weak target compared with the traditional MMPF-TBD, in addition, it is more applicable in low signal-to-noise environment.