Likelihood-gating SMC-PHD filter

To resolve the low computational efficiency of the sequential Monte Carlo (SMC) implementation of the probability hypothesis density (PHD) filter, which requires a large number of particles, we propose an improved SMC-PHD filter called the likelihood-gating SMC-PHD filter. By selecting real observations based on all the predicted particles and using their likelihood values in the SMC-PHD filter updater to substitute for the distances used in the existing gating methods, the proposed filter not only saves time but is also easily implemented in various applications. Experiments show that this filter has excellent real-time performance and better filtering accuracy compared with the basic SMC-PHD filter.