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Particle filters are for the first time introduced into the passive position location estimation. It is shown that the standard particle filter in this case suffers from "sample impoverishment" seriously. Adaptive resampling and regularization operations are suggested to solve the problem. Numerical simulations show that under large initialization errors, the particle filtering significantly improves the location estimation performance compared with the conventional estimation methods.