Relative navigation is a key feature in the joint tactical information distribution system(JTIDS).A parametric message passing algorithm based on factor graph is proposed to perform relative navigation in JTIDS.First of all,the joint posterior distribution of all the terminals’ positions is represented by factor graph.Because of the nonlinearity between the positions and time-of-arrival(TOA) measurement,messages cannot be obtained in closed forms by directly using the sum-product algorithm on factor graph.To this end,the Euclidean norm is approximated by Taylor expansion.Then,all the messages on the factor graph can be derived in Gaussian forms,which enables the terminals to transmit means and covariances.Finally,the impact of major error sources on the navigation performance are evaluated by Monte Carlo simulations,e.g.,range measurement noise,priors of position uncertainty and velocity noise.Results show that the proposed algorithm outperforms the extended Kalman filter and cooperative extended Kalman filter in both static and mobile scenarios of the JTIDS. Relative navigation is a key feature in the joint tactical information distribution system (JTIDS). A parametric message passing algorithm based on factor graph is proposed to perform relative navigation in JTIDS. First of all, the joint posterior distribution of all the terminals’ positions is represented by factor graph.Because of the nonlinearity between the positions and time-of-arrival (TOA) measurement, messages can not be obtained in closed forms by directly using the sum-product algorithm on factor graph.To this end, the Euclidean norm is approximated by Taylor expansion. Chen, all the messages on the factor graph can be derived in Gaussian forms, which enables the terminals to transmit means and covariances. Finally, the impact of major error sources on the navigation performance are evaluated by Monte Carlo simulations, eg, range measurement noise, priors of position uncertainty and velocity noise. Results show that the proposed algorithm outperforms the extended Kalman filter and cooperati ve extended Kalman filter in both static and mobile scenarios of the JTIDS.