We address the problem of multi-sensor multi-object tracking with unknown state parameter information. We develop algorithms capable of dealing with unknown time-dependent object and measurement cardinality and object identities. Additionally, given the dependent observations from sensors, our model takes advantage of the additional information to improve tracking performance. We robustly estimate the evolving objects and measurement cardinality with use of nonparametric modeling. In particular, we employ a dependent Dirichlet process to provide a prior on the time-varying object state distributions, and a hierarchical Dirichlet process (HDP) mixture to model measurement dependency. We demonstrate through simulations that providing multimodal dependent measurements the proposed method can accurately estimate the trajectory of objects and robustly determine the time-dependent cardinality. We also compare this method to the dependent Dirichlet process evolutionary Markov modeling (DDP-EMM) and demonstrate its improved tracking performance.