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Passive coherent location (PCL) has attracted widespread attention due to its low cost, good concealment, and strong anti-stealth. When multiple illuminators operating in a single frequency network are used in a PCL system for multitarget tracking, the three-dimensional (3D) data association uncertainty among the illuminators, measurements and targets has to be dealt with because it will significantly increase computational complexity and reduce scalability. To solve this problem, a scalable multi-association belief propagation (MA-BP) approach for multitarget tracking is proposed. By factorizing the joint probability density function, the tracking problem is described by a factor graph, in which two new illuminator-target oriented and measurement oriented association variables are defined to describe the 3D data association uncertainty. Furthermore, under the Gaussian assumption, a Gaussian version of MA-BP, named as multi-association Gaussian belief propagation (MA-GaBP), is implemented with a generalized virtual measurement model. The MA-GaBP approach can further reduce memory requirements and computational complexity. Simulation results show that the proposed approach effectively solves the 3D data association uncertainty, and has satisfied tracking accuracy and scalability. [ABSTRACT FROM AUTHOR]