In the master-slave collaborative navigation problem of UAVs in satellite rejection environment，it is difficult for the host to perform autonomous navigation and cannot meet the performance requirements of a single machine in collaborative navigation to provide accurate navigation information for the slave and the consistency of trajectories between multiple machines needs to ensure consistent states，and differences in the perception of the same state among multiple machines can lead to divergent trajectories. To address these issues，firstly，combining polarization orientation and optical flow positioning technology，a self navigation method that integrates polarized light/optical flow/IMU is designed to meet the performance requirements of a single machine in a multi machine system. Secondly，based on the particle filter，a relative state consistency filtering method is designed to improve the consistency between the slave and the host state，so as to improve the trajectory consistency between multiple machines. Finally，a simulation and outdoor real machine experimental platform is built. The experimental results show that，compared with the traditional Kalman filtering method，the root mean square error of position is reduced by 57%，the velocity error is reduced by 42.5%，and the course error is reduced by 73%. The outdoor experiment verifies that the method can effectively improve the consistency of the relative state and trajectory between multiple aircrafts.