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Target tracking using non-threshold raw data with low signal-to-noise ratio is a very difficult task, and the model uncertainty introduced by target’s maneuver makes it even more challenging. In this work, a multiple-model based method was proposed to tackle such issues. The method was developed in the framework of Bernoulli filter by integrating the model probability parameter and implemented via sequential Monte Carlo (particle) technique. Target detection was accomplished through the estimation of target’s existence probability, and the estimate of target state was obtained by combining the outputs of modeldependent filtering. The simulation results show that the proposed method performs better than the TBD method implemented by the conventional multiple-model particle filter.

Detection and tracking of multi-target with unknown and varying number is a challenging issue, especially under the condition of low signal-to-noise ratio (SNR). A modified multi-target track-before-detect (TBD) method was proposed to tackle this issue using a nonstandard point observation model. The method was developed from sequential Monte Carlo (SMC)-based probability hypothesis density (PHD) filter, and it was implemented by modifying the original calculation in update weights of the particles and by adopting an adaptive particle sampling strategy. To efficiently execute the SMC-PHD based TBD method, a fast implementation approach was also presented by partitioning the particles into multiple subsets according to their position coordinates in 2D resolution cells of the sensor. Simulation results show the effectiveness of the proposed method for time-varying multi-target tracking using raw observation data.

Non-rigid point matching has received more and more attention. Recently, many works have been developed to discover global relationships in the point set which is treated as an instance of a joint distribution. However, the local relationship among neighboring points is more effective under non-rigid transformations. Thus, a new algorithm taking advantage of shape context and relaxation labeling technique, called SC-RL, is proposed for non-rigid point matching. It is a strategy that joints estimation for correspondences as well as the transformation. In this work, correspondence assignment is treated as a soft-assign process in which the matching probability is updated by relaxation labeling technique with a newly defined compatibility coefficient. The compatibility coefficient is one or zero depending on whether neighboring points preserving their relative position in a local coordinate system. The comparative analysis has been performed against four state-of-the-art algorithms including SC, ICP, TPS-RPM and RPM-LNS, and the results denote that SC-RL performs better in the presence of deformations, outliers and noise.