Standard particle filters can produce bad localization result if the initial particle generation step produces no particle that is close to location (and bearing) of tracked object. The accuracy depends on large number of particles to create at least one particle that's very close to state of tracking object.
Could we introduce in resampling stage a small number of completely random new particles? For example, 99% of particles are randomly selected with weighted probability, while 1% are new particles with random state.
My reasoning is that new particles that are bad guesses would quickly disappear, while good guesses would improve accuracy beyond what was possible with fixed particle pool. Does this improvement to particle filters make sense?