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Marginalized particle filter is an efficient estimation method for navigation and target tracking. The purpose of this paper is to study the Marginalized filter algorithm with time-varying unknown measurement noise variance. The design method is to achieve state dimensionality reduction and estimation of state and measurement variance respectively by using Rao–Blackwellised idea. The measurement distribution model is set as the robust student t-distribution, and the particle weights are obtained through the measurement likelihood model. In this paper, a real-time recursive estimation of the variance parameters of measurement noise is performed by combining the mixed filtering scheme with the Variational inference method. In the resampling stage, the particle weights are resampled together with the state and noise parameters, as a result, robust marginalized particle filter is presented after the state and noise parameters are estimated. Through the simulation analysis of two time-varying cases of gradual change and abrupt change of measurement noise variance of the given target motion model, the conclusion that the performance of the proposed algorithm is better than that of the marginalized particle filter in the case of time-varying measurement variance is verified.