As the number of navigating individuals increases in the robot network, how to determine the navigation platform participating in the network and obtain the state and measurement propagation for probabilistic data association are the key to realize small-scale multi-motion platform cooperative navigation. In this article, a multi-platform collaborative navigation method in the network based on the belief propagation for probabilistic data association is proposed. The article uses random finite sets to model states and measurements and formulate the labeled multi-Bernoulli particle filters. A multi-platform collaborative navigation method in the network using the absolute observation of the moving target platform and the sensor station, and the relative observation of the mobile target platform and the adjacent platform, two kinds of observations are data fusion, and the message passing in the system are analyzed by belief propagation on particle filter. The non-parametric belief propagation algorithm extends factor as an approximation of the belief algorithm. Based on non-parametric belief propagation, the belief propagation probability data association on particle filter algorithm is realized to estimate the state of the navigation system. The simulation results show that the non-parametric belief propagation algorithm has poor performance in different base stations and different particle numbers. Comparatively, the proposed algorithm is less affected by the number of base stations and particles and has good robustness and convergence. Compared with the previous method, the algorithm achieves a root mean square error of less than 0. 3 cm, and 10-time increase in accuracy. The location information of the navigation platform can be effectively retrieved.