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On-line state and parameter estimation is important and difficult in passive ranging. This paper proposes particle filter based on sequential Monte Carlo method for state estimation. And a kernel smoothing approach is introduced for the estimation of static model parameters. To demonstrate effectiveness of the proposed algorithms, the static parameters are calculated by kernel smoothing and states are estimated by Auxiliary Particle Filter (APF) in simulation experiment. The proposed algorithm achieves combined state and parameter satisfactory results.