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Uncertainties unavoidably exist in modeling for nonlinear systems: state equation, measurement equation, and/or noises statistics might be uncertain. Such model mismatches render the performance of nominally optimal state estimators being deteriorated or even unsatisfactory. Therefore, robust filters that are insensitive to modeling uncertainties have to be designed. The challenge is to quantitatively describe the uncertainties and then design accordingly efficient robust filters. Since uncertainties in nominal models make prior state distributions and likelihood distributions uncertain as well, this article proposes a distributionally robust particle filtering framework for nonlinear systems subject to modeling uncertainties. Specifically, we use worst-case prior state distributions (near the nominal prior state distributions) to generate prior state particles and/or determine their weights. Likewise, worst-case likelihood …