In order to land on aircraft carrier autonomously, the altitude of UAV relative to the carrier must be measured precisely to determine the landing position. Traditional GPS/INS integrated navigation system can not satisfy the requirement since it has low precision on estimating the height of UAV when it is below 30 m, and is not capable to locate the target area. Therefore, we proposed a new integrated navigation method combining Millimeter Wave Radar (MWR) with GPS/INS. Aiming to solve the problem of unknown parameter estimation in MWR/GPS/INS integrated navigation system, Extend Particle Filtering (EPF) algorithm was proposed, which was parallel with Extend Kalman Filtering (EKF) algorithm. EPF algorithm modeled unknown parameters by Gaussian random walk process, regarded unknown parameters in dynamic mechanical system as a part of state variations, and then estimated the state variations in extend nonlinear dynamic mechanical system by particle filtering algorithm. Simulation verified the effectiveness of the proposed algorithm and the feasibility of using the integrated navigation system in UAV autonomous landing.