A Novel Particle Filtering Framework Using Genetic Monte Carlo Sampling
Long Ye, Jingling Wang, Chuanzhen Li, Hui Wang, Qin Zhang · 2009
Particle degeneration is a key issue in the performance of a particle filter. In this paper we introduce genetic Monte Carlo into sampling process with the basic idea of solving particle degeneration by means of evolution thought. It is shown that the novel particle filtering framework can effectively eliminate particle degeneration and reduce its dependency on the particle validity. Furthermore, the new genetic particle filter can be optimized by three key genetic factors - selection, crossover and mutation probabilities.