In this paper, an artificial physics optimized particle filter(APO-PF) is proposed. Artificial physics optimization
(APO) is incorpoarted into resampling process to deal with the sample degeneration and impoverishment of generic
particle filter(PF). Virtual force model of the APO specifies that particles have mutually attractive and repulsive force.
Through optimization, the APO can distribute particles in high likelihood area, and the sample degeneration is reduced.
Synchronously, the APO makes particles contain each other in the motion process, ulteriorly formats better coverage to
posterior probability density, which improves estimation performance, particle overlap is avoided and diversity of particle
is also maintained. The simulations are performed to show the effectiveness of the algorithm, and the results show that the
estimation performance and robustness of the proposed algorithm are superior to that of the generic PF.