Static and dynamic distributed interacting multiple model (IMM) fusion algorithms for multiplatform multisensor tracking are developed. Each platform contains a model set, which may or may not be the same as that of other platforms. An interacting multiple model filtering is performed on each platform. An equivalent platform model and an equivalent global model are constructed. To save the bandwidth of an interplatform communication datalink, only combined IMM tracks are allowed to communicate. Taking advantage of the equivalent models, both static and dynamic fusion algorithms have very decent and comparable results. But simulations show that the computation complexity for the dynamic fusion algorithm is far lower than that of the static fusion algorithm. Both algorithms benefit from multiple models and distributed tracking.