To solve the ineffective performance of passive array tracking, this paper presents an interacting multiple model particle filter algorithm (IMM-PF) by combining the interacting multiple model with the particle filter method together. In using this algorithm, the structure of multiple models is adopted to track arbitrary maneuvering of the target, and at the same time particle filter method is employed in each model to deal with the nonlinear/non-Gaussian problems. After interaction and particle filtering, particles in each model with the fixed number are re-sampled to reduce the degeneracy of filtering. First, in the interaction stage, the particles corresponding to each model are input and interacting. Then, estimation resample is obtained by picking out N sampling points in the filtering stage, thereby the estimation output and the related function are gained. In the combination stage, the posteriori probability density functions of the state vectors are obtained, by combining the probability density functions of the different modes taking into account the mode probabilities. In the simulations, by comparison with the general interacting multiple model, the results demonstrate the correctness and efficiency of this new filtering method.